AI & GenAIPopularFeatured

Generative AI Training in Pune with Placement

Name: Archer Infotech
Address: Flat No. 12, Divyadarshan Housing Society, behind Kothrud Bus Stand Road, near Natraj Gas Agency, Londhe Wada, Chaitanya Nagar, Kothrud, Pune, Maharashtra, 411038, IN
Telephone: +91 9850 678451
Price range: ₹₹
Rating: 5

Pune's trusted Generative AI classes at the Archer Infotech institute, Kothrud — weekday, weekend and online batches with placement assistance.

Master generative AI technologies including LLMs, image generation, and building AI-powered applications.

5.0 from 126+ Google reviews

Trained: 10000+
Placed: 5000+
Placement rate: 90%
17+ years: Since 2009

3 Months

Intermediate

Online & Offline

Curriculum last reviewed: 11 June 2026

Interested in this course?

Get in touch with us to learn more about the curriculum, batch timings, and fees.

Enquire Now +91 9850 678451

Next batch starting soon!

Our Generative AI students get placed at

Tech Mahindra

TCS

Infosys

Wipro

Cognizant

Accenture

Capgemini

Persistent Systems

e-Zest Solutions

L&T Infotech

VSpace Software

iVision Software

And many more — 100+ corporate partners hiring across Pune and India.

Generative AI has shifted from research curiosity to mainstream production engineering — every Pune fintech, healthtech, SaaS company, and BFSI shop is now shipping LLM features in customer-facing products. AI Engineer, Applied AI Engineer, and GenAI Solutions Architect titles are the highest-paying technical roles for new entrants in Pune as of May 2026. Archer Infotech's Generative AI training in Pune teaches the discipline as it is actually practiced — Claude (Sonnet 4.6 / Opus 4.7), GPT-5 / GPT-4.1, Gemini, Llama 3.x and Mistral on the open side, retrieval-augmented generation with vector databases, agentic workflows with tool use, parameter-efficient fine-tuning, evaluation discipline, plus the production engineering layer (FastAPI, Docker, observability, cost control). Classroom in Kothrud, online live, and weekend batches available.

Why Learn Generative AI in 2026

Generative AI has moved from 'demo at a conference' to 'in production at every Pune product company' in under three years. Indeed Pune lists more than 400 active AI Engineer / GenAI Engineer / Applied AI Engineer openings as of May 2026 — a tripling from May 2024. Persistent Systems, BMC Software, Bajaj Finserv, Tiger Analytics, Fractal Analytics, ZS Associates, BMW TechWorks India, Mercedes-Benz R&D India, and the captive innovation arms (TCS Research, Infosys Topaz, Mastercard Pune Tech Hub) are hiring continuously. Compensation for AI Engineers with demonstrable production work runs at the very top of Pune's IT corridor — Senior AI Engineers regularly earn ₹30–60 lakh per year, comparable to Senior ML Engineers and ahead of equivalent-experience full-stack developers.

What changed in 2026: the field has stabilised enough to teach. Claude Sonnet 4.6 / Opus 4.7 and GPT-5 / GPT-4.1 are the dominant frontier models for Pune production work, with Gemini 2.5 Pro a strong third. Llama 3.x and Mistral on the open-source side have closed the quality gap for many enterprise use cases (with the privacy advantage of running on-prem). Anthropic's Model Context Protocol (MCP), OpenAI's function calling, and tool-use frameworks (Pydantic-AI, LangChain, LlamaIndex) have settled the agent design pattern. Vector databases — pgvector, Weaviate, Chroma, Pinecone — are commodity. Evaluation has become non-negotiable; Pune CTOs are asking for RAGAS / DeepEval scores, not vibes-based demos.

What this means for hiring: 2026 Pune AI Engineer JDs expect demonstrable LLM API work with at least one frontier model (Claude / GPT / Gemini), one production RAG pipeline with measured retrieval quality, basic agent / tool-use patterns, prompt engineering at the system-prompt + few-shot level, evaluation discipline (RAGAS or equivalent), and FastAPI + Docker for serving. Senior roles add LoRA / QLoRA fine-tuning and observability for LLM calls (latency, cost, hallucination rate). Archer Infotech's curriculum is rebuilt around exactly these expectations — engineering-first, evaluation-aware, frontier-model + open-source coverage.

400+ active AI Engineer / GenAI Engineer roles on Indeed Pune (May 2026)
Claude Sonnet 4.6 / Opus 4.7 + GPT-5 / 4.1 + Gemini 2.5 Pro — frontier defaults
Llama 3.x + Mistral — open-source for on-prem and privacy-sensitive workloads
RAG + Agents + tool use + MCP — the 2026 design vocabulary
Senior AI Engineer compensation regularly hits ₹30–60 lakh in Pune

Who This Course Is For

For You If

Engineering, BCS, MCA, or BSc-CS student targeting AI Engineer / GenAI Engineer / Applied AI Engineer roles
Working backend / full-stack developer wanting to add GenAI to your skill stack
Working data scientist or ML engineer wanting to add the LLM / RAG / agent layer
Product manager or solutions architect wanting hands-on depth before commissioning AI features
Domain expert (legal, medical, financial, education) wanting to ship a GenAI product in your domain
Career restarter targeting AI Engineer as a high-demand re-entry path

Not For You If

If you have no Python experience — take our Python course first; this course assumes Python fluency from week 1
If you expect a guaranteed ₹25L+ AI Engineer offer with no portfolio — Pune fresher AI Engineer entry sits at ₹6–12 lakh; the ₹25L+ roles need 2–3 years and demonstrable production deployments
If you cannot put in 10–12 hours per week of practice outside class — GenAI changes weekly; you need active engagement
If you want certificate-only learning with no projects — Pune AI hiring screens hard on actual deployed work
If your goal is purely deep-learning research / training foundation models — pick a research programme; this course is application engineering, not training-from-scratch
If you have done a Master's in NLP / ML with 2+ years of LLM production work — talk to us about specialised consulting / corporate training instead

Detailed Curriculum

Foundations — Transformers, LLMs, the 2026 Model Landscape

Week 1

What an LLM actually is — at the level you need to build with one, not the level you need to publish a paper. Cover the transformer architecture (attention, positional encoding, layer norm, residual streams) at an intuition level, the difference between pre-training, instruction tuning, and RLHF / DPO, the model-family landscape (Claude, GPT, Gemini, Llama, Mistral, Phi, Qwen), and why specific models suit specific use cases (Claude for long-context analysis, GPT for general tasks, Gemini for multimodal, Llama / Mistral for on-prem and privacy). Plus the discipline that good AI Engineers practice — what a model is bad at, where hallucinations come from, and the cost / latency / quality triangle.

Transformer architecture intuition — attention and residual streamPre-training, instruction tuning, RLHF / DPOModel families — Claude, GPT, Gemini, Llama, Mistral, Phi, QwenFrontier-model selection criteriaHallucinations — sources and mitigationsCost / latency / quality trade-offs

Prompt Engineering & Structured Output

Week 2

Prompt engineering as a real engineering discipline, not magic incantations. Cover system prompts vs user prompts, few-shot prompting, chain-of-thought and the limits thereof, role / persona prompts (and why most production systems should not use them), JSON mode / structured output / Pydantic-AI for type-safe LLM responses, response constraints and validators, prompt versioning and A/B testing, and the discipline of writing prompts as code (in Git, with tests, with metrics). Hands-on with Claude, GPT, and Gemini APIs side-by-side so you internalise their differences.

System prompts vs user prompts — when each is rightFew-shot and chain-of-thought promptingStructured output — JSON mode, Pydantic-AI, response_formatOutput validation and retry patternsPrompt versioning, A/B testing, telemetryAnthropic / OpenAI / Google API SDKs hands-on

Embeddings, Vector Databases & Semantic Search

Week 3

The retrieval half of retrieval-augmented generation. Cover sentence embeddings (sentence-transformers, BAAI BGE, OpenAI text-embedding-3, Voyage), the geometry of embedding space, vector databases (pgvector for SQL-native, Chroma for prototyping, Weaviate / Pinecone / Qdrant for scale), distance metrics (cosine, dot product, L2), HNSW indexing, hybrid retrieval (BM25 + dense + reranking with cross-encoders), and chunking strategies (fixed-size, semantic, parent-document). We finish with a small semantic-search service against a real corpus of your choice.

Embedding models — sentence-transformers, BGE, OpenAI, VoyageVector geometry — cosine, dot product, L2pgvector, Chroma, Weaviate, Pinecone, QdrantHNSW indexing and approximate nearest neighbourChunking — fixed, semantic, parent-documentHybrid retrieval — BM25 + dense + rerankerCross-encoders for reranking

Retrieval-Augmented Generation (RAG) Done Right

Week 4

RAG is the dominant production GenAI pattern in Pune product engineering — and the pattern most poorly executed in the field. Cover the full pipeline: ingestion (PDFs, HTML, code, images via vision models), chunking, embedding, storage, query rewriting, retrieval, reranking, prompt assembly, generation, citation, and response validation. The discipline that separates working RAG from theatre — chunk-size experimentation, retrieval recall measurement, hybrid retrieval, query rewriting for vague questions, and citation-aware generation. Build a production-style RAG service with measured retrieval quality.

End-to-end RAG architectureDocument ingestion — PDFs, HTML, code, vision-OCRQuery rewriting and expansionHybrid retrieval (BM25 + dense + reranker)Prompt assembly with citation tagsCitation-aware generationAnti-patterns — when RAG doesn't helpMulti-tenant RAG and access control

Agents, Tool Use & Model Context Protocol

Week 5

Agentic workflows — LLMs that call tools, query databases, hit APIs, and loop until a goal is satisfied. Cover OpenAI function calling, Claude tool use, Anthropic's Model Context Protocol (MCP) for tool federation, the agent design loop (think → act → observe → think), task decomposition patterns, ReAct, error handling, and the honest limits of agents in production today (cost, latency, debuggability). Build a multi-tool agent that combines retrieval, computation, and external APIs against a real-world workflow.

OpenAI function calling and Claude tool useModel Context Protocol (MCP) basicsAgent loops — ReAct, Plan-and-ExecuteTask decomposition patternsTool design — schemas, error handling, idempotencyMulti-step memory and conversational stateHonest limits — cost, latency, debuggability

Frameworks — LangChain, LlamaIndex, Pydantic-AI

Week 6

Frameworks are tools, not religion. Cover the working subset of LangChain (chains, runnables, LangGraph for state machines), LlamaIndex (best-in-class for RAG over heterogeneous data), and Pydantic-AI (type-safe agent design — the framework Pune Python teams have been gravitating to in 2026). Honest comparison with hand-rolled stacks — when frameworks save time, when they obscure debugging, and when senior AI Engineers reach for vanilla SDK calls instead. Build the same small project in two of the three frameworks to feel the difference.

LangChain — chains, runnables, LangGraphLlamaIndex — for heterogeneous RAGPydantic-AI — type-safe agent designVanilla SDK vs framework — when each winsTracing with LangSmith / LangfuseFramework migration patterns

Evaluation, Observability & Guardrails

Week 7

The week that separates senior AI Engineers from prompt-tinkerers. Cover offline evaluation — RAGAS for RAG quality (faithfulness, answer relevance, context precision / recall), DeepEval for unit-test-style LLM evaluation, human-in-the-loop evaluation patterns. Online observability — Langfuse / LangSmith for tracing, latency / cost / token tracking, structured logging that an SRE can actually debug. Guardrails — content filtering, prompt injection defence, jailbreak resistance, output safety, and the discipline of red-teaming your own system before shipping.

RAGAS — faithfulness, answer relevance, context precision / recallDeepEval — unit-test-style LLM evaluationHuman-in-the-loop evaluationLangSmith / Langfuse tracingLatency / cost / token observabilityPrompt injection and jailbreak defenceContent filtering and output safetyRed-teaming patterns before launch

Open-Source LLMs & Fine-Tuning

Week 8

When and why to leave the frontier APIs. Cover the open-source landscape (Llama 3.x, Mistral, Phi-3, Qwen, IndicBERT for Indian languages), local serving (Ollama for dev, vLLM / TGI for production, llama.cpp for CPU / Apple Silicon), parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) on a single consumer GPU or Colab Pro, dataset preparation for fine-tuning, training discipline (overfitting checks, evaluation), and the honest comparison — when fine-tuning a small open model beats prompting a frontier model (privacy, cost, latency, domain register) and when it doesn't.

Open-source landscape — Llama 3.x, Mistral, Phi-3, Qwen, IndicBERTLocal serving — Ollama, vLLM, TGI, llama.cppPEFT — LoRA, QLoRA — on Colab Pro / single consumer GPUDataset preparation for fine-tuningEvaluation post-fine-tuneFrontier API vs fine-tuned open — the honest comparison

Multimodal — Images, Audio, Video, Code

Week 9

Beyond text. Vision-language models (Claude vision, GPT-4-vision, Gemini multimodal) for document understanding, OCR, and visual QA. Image generation (DALL-E 3, Imagen, Stable Diffusion 3, Flux) and the production patterns (asset pipelines, brand-safe filtering, costs). Speech (Whisper for transcription, ElevenLabs for synthesis), code generation (Claude / GPT for code, the honest 'pair programmer' pattern), and the multimodal RAG pattern (text + image retrieval). Plus a cost-conscious approach since multimodal calls run 5–10× the cost of text-only.

Vision-language models — Claude / GPT / Gemini visionOCR and document understandingImage generation — DALL-E 3, Imagen, Stable Diffusion, FluxSpeech — Whisper, ElevenLabsCode generation patternsMultimodal RAGCost discipline for multimodal

Production GenAI — FastAPI, Docker, Observability, Cost

Week 10

The week that turns a notebook into a service. FastAPI as the de facto serving layer for GenAI in Python (async endpoints, Pydantic v2, JWT auth, streaming responses with Server-Sent Events, WebSocket for chat). Docker for containerisation, GitHub Actions CI/CD with model-call mocking in tests, observability (Langfuse + Prometheus + Grafana for production-grade GenAI telemetry), and FinOps for LLM calls — token budgets per request, caching (semantic cache + exact cache), prompt compression, and the patterns that cut a runaway OpenAI / Anthropic bill by 50–70% without changing model quality.

FastAPI for GenAI — async, streaming, WebSocketServer-Sent Events for streaming responsesDocker containers for inferenceGitHub Actions CI/CD with API mockingLangfuse + Prometheus + Grafana telemetryToken budgets, semantic caching, prompt compressionCost dashboards and alerting

Capstone Project & Interview Preparation

Weeks 11–12 + 2 weeks placement prep

Two weeks of full-time capstone work plus structured interview preparation. Pick one of three capstone projects (see Capstone Projects). Mock interviews calibrated for Pune AI Engineer hiring panels — Persistent, BMC, Bajaj Finserv, Tiger Analytics, Fractal, BMW TechWorks, Mercedes-Benz R&D, TCS Research, Infosys Topaz, Mastercard Pune Tech Hub. Includes a system-design round (design a customer-support assistant for a BFSI company), an evaluation round (how would you measure if this RAG is working?), and a behavioural / product-thinking round. Resume / LinkedIn / GitHub polish included.

Capstone implementation, deployment, READMECode review with the lead trainerAI system-design mock roundEvaluation-thinking mock roundBehavioural and product-thinking roundResume + LinkedIn rewrite for AI Engineer JDsGitHub portfolio polish — RAG with measured retrieval recall, agent demosHR mock interview and salary negotiation

Capstone Projects You Will Build

Project 1: Production RAG Service with Measured Retrieval Quality

Pick a real domain corpus (Indian legal documents, medical guidelines, internal product documentation, RBI / SEBI regulations, or a public dataset). Build a complete RAG service — ingestion pipeline (PDFs + HTML), semantic chunking, hybrid retrieval (BM25 + dense + reranker), query rewriting for vague questions, citation-aware generation with Claude or GPT, FastAPI service with streaming responses, Langfuse tracing, and RAGAS evaluation showing measured faithfulness / context precision / answer relevance scores. Outcome: a public GitHub repository with a clickable demo URL plus an evaluation report — exactly the artefact Pune AI Engineer hiring panels interview on.

Claude / GPT APIpgvector or WeaviateBAAI BGE embeddings + cross-encoder rerankerFastAPI with streamingLangfuse tracingRAGAS evaluationDocker + GitHub ActionsAWS or Render deployment

Project 2: Multi-Tool Agent with MCP / Function Calling

Build a domain-specific assistant that uses multiple tools — retrieval over a corpus, SQL queries against a database, REST API calls to internal services, and computational tools (calculators, validators). Implement using either Anthropic Model Context Protocol or OpenAI function calling. Includes graceful error handling, retry patterns, conversational memory, observability via Langfuse, and a Streamlit or React frontend for demo. Pick a real workflow — customer-support assistant, sales-prep assistant, financial-analysis assistant. Outcome: an agent that demos in 5 minutes and signals senior-AI-Engineer thinking on the resume.

Claude tool use or OpenAI function callingModel Context Protocol (optional)Pydantic-AI or LangGraphPostgreSQL + pgvectorFastAPI backendStreamlit or React frontendLangfuse observability

Project 3: Fine-Tuned Open-Source LLM for Domain Use Case

Take an open-source model (Llama 3.1 8B, Mistral 7B, or Phi-3) and fine-tune it via LoRA / QLoRA on Colab Pro for a specific domain register — Indian legal language, medical SOAP notes, customer-support tone, or financial summarisation. Includes proper dataset preparation, training discipline (validation curves, early stopping), evaluation comparing fine-tuned model against base model AND against a frontier model on the same task, and serving via vLLM or Ollama. Outcome: a 2026-relevant fine-tuning project with documented evaluation — the differentiator on senior AI Engineer JDs and the artefact most Pune AI candidates lack.

Llama 3.1 / Mistral / Phi-3PEFT — LoRA / QLoRAHugging Face Transformers + datasets + accelerateColab Pro / Kaggle GPUvLLM or Ollama servingEvaluation against frontier baseline

Career Outcomes & Salaries in Pune

AI Engineer, GenAI Engineer, and Applied AI Engineer are the highest-paid technical roles for new entrants in Pune in 2026 — Indeed Pune lists 400+ active openings, tripling from 2024, with continuous hiring at Persistent Systems, BMC Software, Bajaj Finserv, Tiger Analytics, Fractal Analytics, ZS Associates, BMW TechWorks India, Mercedes-Benz R&D India, TCS Research and Innovation, Infosys Topaz, Wipro AI&I, and the Mastercard Pune Tech Hub. Compensation has separated from generic 'Software Engineer' titles — Senior AI Engineers regularly earn ₹30–60 lakh per year because the role bundles modelling literacy with production engineering and product judgement.

What pulls an AI Engineer above the median band: a public GitHub repository with a deployed RAG service AND measured retrieval quality (not just a demo), one agent / tool-use project that demos in 5 minutes, evaluation discipline visible in the README (RAGAS scores, latency / cost dashboards), and one fine-tuning project on an open-source model. Our capstone projects are designed exactly around these signals.

Senior AI Engineer and AI Solutions Architect bands at the top end are reported as national figures (Pune-specific Indeed pages do not exist for these specific titles); Pune trends within ±10% of these figures based on AmbitionBox, 6figr, and direct alumni feedback.

Role	Salary band	Source
AI Engineer (Pune)	₹9,89,000 per year average	Indeed Pune (AI Engineer)
Junior AI Engineer / GenAI Engineer (Pune entry, <2 years)	₹6,00,000 – ₹12,00,000 per year	AmbitionBox Pune AI Engineer
Mid-level AI Engineer (Pune, 3–5 years)	₹16,00,000 – ₹26,00,000 per year	Glassdoor Pune AI Engineer
Senior AI Engineer / Applied AI Engineer (national, 5–8 years)	₹28,00,000 – ₹50,00,000 per year	6figr India Senior AI Engineer (Pune ±10%)
AI Solutions Architect / Lead AI Engineer (national, 8+ years)	₹45,00,000 – ₹80,00,000 per year	Industry aggregation 2026 (Pune ±10%)

Pune companies hiring Generative AI professionals in 2026

Persistent SystemsBMC SoftwareBajaj FinservTiger AnalyticsFractal AnalyticsZS AssociatesBMW TechWorks IndiaMercedes-Benz R&D IndiaTCS Research and InnovationInfosys TopazWipro AI&IMastercard Pune Tech HubMathCoSynechronMphasis NEXT LabsCognizant AI

Roles after this Generative AI course

AI EngineerGenAI EngineerApplied AI EngineerLLM EngineerRAG / Search EngineerAI Application DeveloperPrompt Engineer (with engineering depth)Junior AI Solutions Architect

Course Duration, Batches & Modes in Pune

Duration: 3 months of structured curriculum (12 weeks) plus 2 weeks of capstone project work and interview preparation

Classroom

Archer Infotech, Kothrud, Pune

• Morning batch — 10:00 to 13:00
• Evening batch — 18:00 to 21:00
• Lab access available outside class hours

Online Live

• Same hours as classroom batches — morning or evening
• Recordings available for review
• Same code reviews and project feedback as in-person batches

Tools used:

Zoom for live sessionsGitHub for code reviewsAnthropic + OpenAI + Google API access (each student funds ~₹1,500 of API credits across the course)Google Colab Pro / Kaggle GPU for the fine-tuning weekSlack / WhatsApp for asynchronous Q&A

Weekend

• Saturday + Sunday, 09:00 to 13:00

Stretches over 5 months instead of 3 to accommodate working professionals. Same content, lower weekly load.

Maximum 15 students per batch — small enough that the trainer reviews every student's prompts, retrieval quality, and evaluation reports personally. Classroom batches start every 4 weeks; weekend batches every 6 weeks.

Generative AI Course Fees in Pune

Course fees range from ₹20,000 to ₹90,000 depending on mode (classroom / online / weekend), batch type, and any applicable concession. Kindly reach us for the current 2026 quote — we calibrate by early-bird timing, group enrolment, and returning-alumni concessions. LLM API spend (Anthropic + OpenAI + Google) typically runs ₹1,500 across the course, GPU compute (Colab Pro for fine-tuning) ~₹1,000 — both paid by the student directly.

₹20,000 – ₹90,000 — the higher end covers placement-track classroom batches with full fine-tuning + multimodal modules, frontier-model API access, and extended interview prep; the lower end covers concession-eligible online or weekend formats.

Payment options:

Single payment with early-bird discount
EMI in 2–3 instalments at no extra cost
Corporate sponsorship — invoiced to your employer with GST

Placement Support in Pune

Placement support starts from week 8 of the course, not at the end. By the time you finish the curriculum, your resume highlights real RAG and agent work with measured evaluation, your GitHub has a deployed AI service with a clickable demo URL, and you have completed at least three mock technical interviews against question banks from Pune AI hiring teams.

We say placement support, not placement guarantee — for two honest reasons. First, no institute can guarantee a hire when the final decision is the company's. Second, the institutes that do guarantee tend to bury the conditions in fine print. Our support is unconditional, time-bound (six months after course completion), and includes free re-entry to a future batch's interview-prep sessions if your first round of interviews does not land.

Placement process — week by week

Week 8 — resume and LinkedIn rewrite, calibrated for AI Engineer / GenAI Engineer JDs
Week 9 — GitHub portfolio cleanup, RAG demo deployment links, evaluation reports polish
Weeks 10–11 — AI system-design drills, evaluation-thinking walkthroughs, behavioural / product mock rounds
Weeks 11–12 — three rounds of mock technical interviews
Week 12 — HR mock interview and salary negotiation coaching
Post-course — referrals via our 17-year alumni network at 12+ partner companies
Up to 6 months of continued support after course end
Free re-entry to future batch interview-prep sessions if first round does not land

Partner companies

See recent placement records →

How Archer Infotech Compares

We compare ourselves against typical Pune Generative AI training institutes on factual rows only — no logos, no opinions. Use this as a checklist when evaluating any institute.

Factor	Archer Infotech	Typical Pune institute
Trainers named on course page with photos and LinkedIn	Yes — Vinod Patil and Amol Patil	No — generic 'expert trainers' branding
Frontier models covered hands-on	Claude Sonnet 4.6 / Opus 4.7, GPT-5 / 4.1, Gemini 2.5 Pro — all three SDKs	OpenAI-only, often GPT-3.5 / GPT-4
Open-source LLM fine-tuning	LoRA / QLoRA / PEFT on Llama / Mistral / Phi — capstone-eligible project	Theoretical mention, no hands-on
RAG depth covered	Hybrid retrieval + rerankers + RAGAS evaluation + citation generation	Basic embed-and-retrieve, no evaluation
Agent / tool-use coverage	Function calling, MCP, ReAct, multi-step memory hands-on	Marketing mention only
Evaluation discipline	RAGAS + DeepEval + Langfuse — full week of evaluation engineering	Vibes-based 'looks good' demo only
Multimodal coverage	Vision-language + image gen + speech + multimodal RAG	Image generation demo only
Production engineering pattern	FastAPI streaming + Docker + Langfuse observability + cost dashboards	Notebook-only — no deployment artefact
Public GitHub portfolio output	Yes — deployed AI services with clickable demos and evaluation reports	Notebook screenshots in a PDF
Salary data shown	Cited from Indeed Pune + AmbitionBox + Glassdoor + 6figr with source URLs	Single number with no source
Course fee transparency	₹20,000 – ₹90,000 published range with mode breakdown	Hidden behind enquiry form
Placement support duration after course	6 months, with free re-entry to interview prep	1–3 months or vaguely 'until placed'
Batch size cap	15 students	25–40 students

Compare with whoever you are considering — we welcome the comparison. The right test is whether you can see actual student RAG demos with measured retrieval quality before you pay.

Generative AI vs Machine Learning — Which Should You Pick in Pune?

GenAI vs ML is the most-asked question in Pune AI counselling. The honest distinction: Machine Learning is the broader engineering discipline (algorithms, modelling, deployment, MLOps, including but not limited to LLMs). Generative AI is the specialisation focused on LLMs and generative models — heavier on prompting, RAG, agents, and frontier-model APIs; lighter on classical algorithm depth and from-scratch training. Both ship to production at most Pune product companies; they overlap heavily.

Compensation reality in Pune (May 2026): ML Engineer averages ₹10.32 lakh on Indeed; AI Engineer averages ₹9.89 lakh — close at the average level. The separation appears at the senior end — Senior AI Engineers / Applied AI Engineers running RAG / agent / fine-tuning systems in production are getting ₹28–50 lakh national bands (Pune ±10%), comparable to Senior ML Engineers, with AI Solutions Architect titles pushing into ₹45–80 lakh. The premium is for engineers who can both design AND ship LLM systems with measured quality.

Honest recommendation: pick Generative AI if your goal is shipping LLM-powered products fast, you have backend or full-stack background, and you want the highest-velocity 2026 entry path into AI roles. Pick Machine Learning if your goal is algorithmic depth, classical-ML deployment, or research-flavoured engineering. Either path stacks well with the other — many of our students do GenAI first (faster portfolio, faster placement) and add ML 6–12 months later.

Prerequisites & How to Start

Prerequisites: Python fluency at the level of being able to write a 200-line script without lookup, comfort with REST APIs and JSON, and basic familiarity with at least one SQL or NoSQL database. If you have done our Python or Data Science course (or equivalent), you are ready. Working backend or full-stack developers from any Python / Java / Node background typically slot in well; pure non-developers should do the Python course first.

Decide your mode — classroom in Kothrud, online live, or weekend
Check the upcoming batch dates on our batch schedule page
Book a free 30-minute counselling call — we will honestly tell you whether the course fits your goal (we say no to roughly 15% of GenAI enquirers because Python or backend foundation is not yet there)
Confirm enrolment and complete pre-course orientation (API account creation guide for Anthropic + OpenAI + Google, environment setup)
Show up to day one with a laptop running 64-bit OS, a personal credit card or UPI mandate (for API account verification — billing alarms keep usage in budget)

Frequently Asked Questions

Which is the best Generative AI training institute in Pune?+

We can't honestly answer 'best' for ourselves. The test that works: ask any institute you are considering to (1) name the trainer who will teach your batch and show their LinkedIn, (2) show real student RAG demos with measured retrieval quality (RAGAS scores), and (3) name companies that hired their last 5 batches. Compare on those three.

How long does Generative AI training in Pune take at Archer Infotech?+

Three months (12 weeks) of structured curriculum plus 2 weeks of capstone project and interview preparation. The weekend batch stretches over 5 months at the same content depth, designed for working professionals.

What is the salary of an AI Engineer in Pune?+

Indeed Pune reports an average of ₹9.89 lakh per year for AI Engineer (May 2026). Junior AI Engineer Pune entry sits at ₹6–12 lakh per year per AmbitionBox. Mid-level AI Engineers (3–5 years) earn ₹16–26 lakh per Glassdoor. Senior AI Engineers / Applied AI Engineers (5–8 years) earn ₹28–50 lakh nationally with Pune trending within ±10%. AI Solutions Architects (8+ years) regularly hit ₹45–80 lakh.

What is the fee for the Generative AI course in Pune?+

Course fees range from ₹20,000 to ₹90,000 depending on mode (classroom / online / weekend), batch type, and applicable concession. The higher end covers placement-track classroom batches with full fine-tuning + multimodal modules, frontier-model API access, and extended interview prep; the lower end covers concession-eligible online or weekend formats. LLM API spend (~₹1,500) and GPU compute for fine-tuning (~₹1,000) are paid by the student directly.

Do I need ML or deep-learning background?+

No — we cover the transformer / LLM intuition you actually need (week 1) at a level that anyone with backend / full-stack / Python background can absorb. We focus on application engineering, not training models from scratch. If you do have ML / deep-learning background, you will move slightly faster in weeks 1 and 8 (fine-tuning).

Do I need Python before joining the course?+

Yes — Python fluency is required from week 1. If you have done our Python or Data Science course (or equivalent), you are ready. We do not turn this course into a Python primer; that would short-change the GenAI content.

Generative AI or Machine Learning — which should I pick?+

GenAI for LLM / RAG / agent / prompt-engineering depth and the highest-velocity 2026 entry path into AI roles. Machine Learning for classical algorithm depth, deployment of supervised / unsupervised models, and broader engineering pattern. Both stack well — many students do GenAI first (faster portfolio) and add ML 6–12 months later. Compensation at the senior end is comparable.

Will I work on real projects?+

Yes — three capstone projects: (1) production RAG service with measured retrieval quality (RAGAS evaluation), (2) multi-tool agent with MCP or function calling, (3) fine-tuned open-source LLM for a domain use case. All three become public GitHub repositories with clickable demo URLs and evaluation reports.

Which models are covered — only ChatGPT?+

All three frontier model families hands-on — Claude (Sonnet 4.6 / Opus 4.7), GPT (GPT-5 / GPT-4.1), and Gemini (2.5 Pro) — plus open-source models (Llama 3.x, Mistral, Phi-3) for the fine-tuning week. We deliberately use multiple SDKs side-by-side so you internalise the differences, because Pune production teams pick models per use case rather than committing to one vendor.

Is fine-tuning covered or extra?+

Included in every batch. Week 8 is a full module on parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) on open-source models running on Colab Pro or Kaggle GPU. Capstone Project #3 is a complete fine-tune-and-deploy workflow. This module is what separates 2026 senior Pune AI Engineer hiring from prompt-engineer-only candidates.

Is Anthropic Claude / Model Context Protocol covered?+

Yes — Claude Sonnet 4.6 and Opus 4.7 are first-class throughout the course (alongside GPT-5 / 4.1 and Gemini 2.5 Pro). Anthropic Model Context Protocol (MCP) is covered in week 5 alongside OpenAI function calling — both as the dominant tool-use patterns in 2026. Capstone Project #2 lets you choose either MCP or function calling.

What about evaluation — RAGAS / DeepEval?+

Week 7 is a full module on evaluation discipline — RAGAS for RAG quality (faithfulness, answer relevance, context precision / recall), DeepEval for unit-test-style LLM evaluation, Langfuse for production tracing, and the discipline of red-teaming your own system before launch. Pune AI hiring panels in 2026 specifically test for evaluation thinking, which is the differentiator on senior interviews.

Are weekend GenAI classes available in Pune?+

Yes — Saturday and Sunday, 09:00–13:00, stretched over 5 months instead of 3. Same content, same trainers, same projects. Designed for working professionals who cannot attend weekday batches.

How is this different from your ChatGPT & LLMs / Prompt Engineering / AI Tools courses?+

This Generative AI training is the comprehensive engineering programme — 3 months covering prompting + RAG + agents + fine-tuning + multimodal + production engineering. ChatGPT & LLMs is a 2-month focused track on the OpenAI ecosystem. Prompt Engineering is a 1-month focused course on prompting craft. AI Tools is a 1-month course on using AI tools for productivity. The GenAI course is the full engineering programme; the others are focused subsets.

What support do I get after course completion?+

Six months of active placement support — mock interviews calibrated for AI Engineer / GenAI Engineer roles (system-design + evaluation-thinking + behavioural rounds), referrals via our alumni network at 12+ partner companies, resume / LinkedIn / GitHub rewrites, and salary negotiation coaching. If your first round of interviews does not land, you can sit in on a future batch's interview-prep sessions free of charge.

Are the named trainers actually teaching, or are they just on the brochure?+

Vinod Patil personally leads the LLM foundations, prompt engineering, agents, fine-tuning, and capstone weeks. Amol Patil leads the RAG, frameworks, evaluation, and production engineering weeks. The same names you see on this page show up in your batch on day one.

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Taught by Industry Experts

Every batch is led by a working professional with years of MNC experience.

Vinod Patil

Solutions Architect & AI Trainer

12 years experience

Amol Patil

Senior Corporate Trainer

10+ years experience

Generative AI Training in Pune with Placement

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Why Learn Generative AI in 2026

Who This Course Is For

Detailed Curriculum

Capstone Projects You Will Build

Career Outcomes & Salaries in Pune

Pune companies hiring Generative AI professionals in 2026

Roles after this Generative AI course

Course Duration, Batches & Modes in Pune

Generative AI Course Fees in Pune

Placement Support in Pune

How Archer Infotech Compares

Generative AI vs Machine Learning — Which Should You Pick in Pune?

Prerequisites & How to Start

Frequently Asked Questions

Taught by Industry Experts

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Generative AI careers in Pune — monthly briefing

Ready to Start Your Generative AI Journey?

Detailed Curriculum

Capstone Projects You Will Build

Career Outcomes & Salaries in Pune

Pune companies hiring Generative AI professionals in 2026

Roles after this Generative AI course

Course Duration, Batches & Modes in Pune

Generative AI Course Fees in Pune

Placement Support in Pune

How Archer Infotech Compares

Generative AI vs Machine Learning — Which Should You Pick in Pune?

Prerequisites & How to Start

Frequently Asked Questions