AI & GenAI

ChatGPT & LLMs 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: ₹₹

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

Deep dive into ChatGPT and large language models. Learn to build, fine-tune, and deploy LLM-powered applications.

2 Months

Intermediate

Online & Offline

Curriculum last reviewed: 6 May 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!

ChatGPT and the wider OpenAI ecosystem dominate enterprise AI adoption in Pune — most Pune fintech / SaaS / consumer-tech teams that ship LLM features run primarily on OpenAI APIs (GPT-5, GPT-4.1, Embeddings, Whisper, Sora, plus Custom GPTs and the Assistants API). Archer Infotech's ChatGPT & LLMs training in Pune is the focused track for engineers and product teams that want OpenAI-ecosystem depth — distinct from our broader Generative AI course (which covers Claude / Gemini / open-source as well). The track teaches the OpenAI API surface in detail (Chat Completions, function calling, structured output, Batch API, Realtime API, Vector Stores, Assistants API, Custom GPTs), fine-tuning workflows, plus the production patterns (FastAPI streaming, cost / latency / safety controls, Langfuse observability). Classroom in Kothrud, online live, and weekend batches available.

Why Learn ChatGPT & LLMs in 2026

OpenAI remains the most-deployed frontier-model vendor in Pune product engineering — Indeed Pune lists 300+ active openings that explicitly call out 'OpenAI', 'ChatGPT API', or 'GPT' as required, plus a few hundred more that list it as preferred. The biggest employers shipping OpenAI-powered features are Persistent Systems, BMC Software, Bajaj Finserv, BharatPe Pune, Razorpay Pune, plus the Pune SaaS scene (Amagi, Fyllo, Drip Capital, Innovaccer Pune, Whatfix Pune). Compensation for AI Engineers with demonstrable OpenAI integration runs at the top of Pune's IT corridor.

What changed in 2026: GPT-5 (released late 2025) is the production frontier model, with GPT-4.1 as a faster / cheaper alternative for many use cases. The Assistants API has matured for stateful workflows (Custom GPTs in the consumer product, Assistants API in the developer product). Structured Outputs guarantees JSON-schema compliance. The Batch API offers 50% cost savings for non-real-time workloads. The Realtime API enables voice-first applications. Fine-tuning with reinforcement learning (RFT) has moved from preview to GA. Plus the Vector Store integration in the Assistants API has consolidated the RAG pattern.

What this means for hiring: 2026 Pune ChatGPT / LLM JDs expect OpenAI SDK fluency in Python and TypeScript, function calling and structured outputs, basic RAG implementation, plus the production engineering layer (streaming, cost monitoring, safety filtering). Senior roles add fine-tuning, multi-model orchestration, and evaluation discipline.

300+ active Pune openings explicitly require 'OpenAI' or 'ChatGPT API' (May 2026)
GPT-5 + GPT-4.1 + Assistants API + Realtime API + Batch API — the modern OpenAI surface
Structured Outputs + function calling + Vector Stores — the production patterns
Senior AI Engineer compensation regularly hits ₹30–50 lakh in Pune
Distinct from broader Generative AI course — OpenAI-ecosystem depth

Who This Course Is For

For You If

Working backend / full-stack developer wanting to add OpenAI integration to your skill stack
Working product manager or solutions architect wanting hands-on depth before commissioning AI features
Working AI engineer wanting to deepen specifically on the OpenAI ecosystem
Engineering / BCS / MCA student targeting Pune AI Engineer / GenAI Engineer roles
Domain expert (legal, medical, financial, education) wanting to ship an OpenAI-powered product in your domain

Not For You If

If you have no Python experience — take our Python course first; this assumes Python fluency
If you want broad multi-model AI / GenAI training — take our Generative AI course (covers Claude / Gemini / open-source too)
If you cannot put in 8–10 hours per week of practice outside class
If you want certificate-only learning with no portfolio — Pune AI hiring screens hard on real deployed work
If you have 2+ years of production OpenAI / LLM work — talk to us about advanced fine-tuning / RAG specialisations

Detailed Curriculum

OpenAI Ecosystem Foundations

Week 1

What an LLM is at the level you need to build with one. Cover the OpenAI model family (GPT-5, GPT-4.1, GPT-4o, GPT-3.5-turbo, embeddings, Whisper, Sora, DALL-E 3) and which suits which use case, the OpenAI Python SDK and TypeScript SDK, API key management, plus the Playground for prototyping. By the end of week 1 every student has API keys, a working SDK setup, and has built their first 'Hello, world' chat completion.

OpenAI model family — GPT-5, GPT-4.1, embeddings, Whisper, SoraModel selection criteria — quality / cost / latencyPython SDK + TypeScript SDK setupAPI key managementOpenAI Playground for prototypingCost dashboards and rate limits

Chat Completions, Streaming & Structured Outputs

Week 2

The foundational API surface. Cover Chat Completions (system / user / assistant roles), temperature / top_p / max_tokens, streaming responses (the Server-Sent-Events pattern that real production UIs use), Structured Outputs with JSON schemas (the 2024+ feature that guarantees JSON-schema compliance — replacing the old prompt-engineered 'please output JSON' pattern), plus the discipline of designing prompts as code (in Git, with tests, with telemetry).

Chat Completions APIRoles — system / user / assistantTemperature, top_p, max_tokensStreaming with SSEStructured Outputs with JSON schemaPrompt versioning

Function Calling & Tool Use

Week 3

Function calling — the pattern that lets the LLM invoke real-world tools (database queries, API calls, computations). Cover the JSON-schema description of tools, the request-response loop, parallel tool calling, plus the discipline of designing tools that survive LLM mis-invocation (idempotency, validation, error handling). We finish by building a small assistant that combines retrieval + computation + external APIs.

Function calling — schema descriptionRequest-response loopParallel tool callingTool design — idempotency, validationError handling and retryMulti-step assistants

Embeddings, Vector Stores & RAG

Week 4

Retrieval-Augmented Generation in the OpenAI ecosystem. Cover OpenAI's text-embedding-3-large / -small, the geometry of embedding space, vector storage options (OpenAI Vector Stores for managed RAG, plus pgvector / Chroma / Weaviate / Pinecone for self-hosted), retrieval techniques (semantic + hybrid + reranking), chunking strategies (fixed-size, semantic, parent-document), plus the Assistants API's built-in File Search (the managed RAG path that gets you to a working system in 1 hour).

text-embedding-3-large / -smallOpenAI Vector Storespgvector / Chroma / Weaviate alternativesChunking strategiesHybrid retrieval and rerankingAssistants API File Search (managed RAG)

Custom GPTs, Assistants API & Realtime API

Week 5

The OpenAI productised layer. Cover Custom GPTs (the consumer-product surface — for sales / marketing / internal-tool users), the Assistants API (the developer-product surface — for engineers building stateful applications), threads / messages / runs, plus the Realtime API for voice-first applications (the surface that powers ChatGPT's voice mode, Apple Intelligence-like UX). Honest discussion: when each productised surface is right vs when raw Chat Completions is the better choice.

Custom GPTs — building, sharing, monetisingAssistants API — threads, messages, runsStateful conversationsRealtime API for voiceCustom GPTs vs Assistants API vs raw Chat Completions

Fine-Tuning & Production Engineering

Weeks 6–7

When fine-tuning OpenAI models earns its place — domain register (legal / medical / customer-support tone), task-specific behaviour, cost reduction (fine-tune a smaller model to match a frontier model on your specific task). Cover the OpenAI fine-tuning API for GPT-4o-mini and GPT-3.5-turbo, dataset preparation, evaluation, plus reinforcement fine-tuning (RFT — newly GA). Then production engineering — FastAPI for serving, streaming responses, observability with Langfuse / OpenAI usage dashboards, the Batch API for 50% cost savings on non-real-time work, plus the safety / moderation patterns.

When to fine-tune (and when not)Fine-tuning API for GPT-4o-mini / GPT-3.5-turboDataset preparationReinforcement fine-tuning (RFT)Evaluation post-fine-tuneFastAPI for servingStreaming and Server-Sent EventsLangfuse + OpenAI usage observabilityBatch API for cost savingsSafety / moderation API

Capstone & Interview Prep

Week 8

One week of capstone work plus structured interview preparation. Pick one of three capstone projects (see Capstone Projects). Mock interviews calibrated for Pune AI Engineer hiring panels.

Capstone implementation, deployment, READMEAI system-design mock roundEvaluation / observability discussionResume + LinkedIn rewrite for AI Engineer JDsGitHub portfolio polishHR mock and salary negotiation

Capstone Projects You Will Build

Project 1: Domain RAG Service with Assistants API File Search

A production-style RAG service using OpenAI's Assistants API File Search (the managed RAG path) — pick a real domain corpus (legal / medical / regulatory / product docs), upload via the Files API, build an Assistant with File Search enabled, expose via FastAPI with streaming. Plus citation handling and a small evaluation report (RAGAS or manual eval). Outcome: a public GitHub repository with deployed demo URL.

OpenAI Assistants API + File SearchFastAPI with streamingPython 3.13Langfuse tracingRender or Vercel deployment

Project 2: Multi-Tool Agent with Function Calling

An agent that uses function calling to combine retrieval, SQL queries, REST APIs, and computational tools — pick a domain workflow (sales prep, customer support, financial analysis). Includes graceful error handling, conversation memory, observability via Langfuse, and a Streamlit or React frontend.

OpenAI function callingFastAPI backendStreamlit or React frontendPostgreSQL + pgvectorLangfuse observability

Project 3: Fine-Tuned GPT-4o-mini for Domain Use Case

Fine-tune GPT-4o-mini on a domain dataset — Indian legal language, medical SOAP notes, customer-support tone, or financial summarisation. Includes proper dataset preparation, fine-tuning via the OpenAI API, evaluation comparing fine-tuned vs base model on the same task, plus deployed demo.

OpenAI fine-tuning APIGPT-4o-miniCustom evaluation harnessFastAPI deployed demo

Career Outcomes & Salaries in Pune

AI Engineer / GenAI Engineer / OpenAI integration roles are among the highest-paid technical specialisations in Pune in 2026 — Indeed Pune lists 300+ openings explicitly requiring OpenAI / ChatGPT API. The biggest employers are Persistent Systems, BMC Software, Bajaj Finserv, BharatPe Pune, Razorpay Pune, plus the Pune SaaS scene.

What pulls an OpenAI-specialised engineer above the median band: a public GitHub repository with at least one deployed RAG service with measured retrieval quality, demonstrable function-calling depth, one fine-tuning project, plus production engineering (FastAPI streaming + observability + cost control). Our capstone projects are designed exactly around these signals.

Senior AI Engineer 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.

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 (national, 5–8 years)	₹28,00,000 – ₹50,00,000 per year	6figr India Senior AI Engineer (Pune ±10%)

Pune companies hiring ChatGPT/LLMs professionals in 2026

Persistent SystemsBMC SoftwareBajaj FinservBharatPe (Pune)Razorpay (Pune)Pine Labs (Pune)AmagiFylloDrip CapitalInnovaccer (Pune)Whatfix (Pune)Tiger AnalyticsFractal AnalyticsTCS Research and InnovationInfosys TopazMastercard Pune Tech Hub

Roles after this ChatGPT/LLMs course

AI Engineer (OpenAI specialisation)GenAI EngineerLLM Application DeveloperPrompt Engineer (with engineering depth)RAG EngineerJunior AI Solutions Architect

Course Duration, Batches & Modes

Duration: 8 weeks of structured curriculum (~2 months total)

Classroom

Archer Infotech, Kothrud, Pune

• Morning batch — 10:00 to 13:00
• Evening batch — 18:00 to 21:00

Online Live

• Same hours as classroom batches
• Recordings available for review

Tools used:

Zoom for live sessionsOpenAI API access (each student funds ~₹1,500 of API credits)GitHub for code reviewsSlack / WhatsApp for async Q&A

Weekend

• Saturday + Sunday, 09:00 to 13:00

Stretches over ~3.5 months instead of 2.

Maximum 15 students per batch.

Course Fees

Course fees range from ₹20,000 to ₹90,000 depending on mode and concession. OpenAI API spend (~₹1,500 across the course) is paid by the student directly.

₹20,000 – ₹90,000

Payment options:

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

Placement Support

Placement support starts from week 6. By the end of the curriculum your resume highlights real OpenAI-integrated services with measured evaluation, your GitHub has at least two production-style repositories, and you have completed at least two mock technical interviews focused on AI Engineer roles.

We say placement support, not placement guarantee. Our support is unconditional, time-bound (six months after course completion), and includes free re-entry to a future batch's interview-prep sessions.

Placement process — week by week

Week 6 — resume + LinkedIn rewrite for AI Engineer JDs
Week 7 — GitHub portfolio cleanup, demo URLs, evaluation reports
Week 8 — two rounds of mock technical interviews
Week 8 — HR mock and salary negotiation coaching
Post-course — referrals via our 17-year alumni network
Up to 6 months of continued support
Free re-entry to future batch interview-prep sessions

Partner companies

Persistent SystemsBMC SoftwareBajaj FinservBharatPe (Pune)Razorpay (Pune)AmagiTiger AnalyticsFractal AnalyticsTCS Research and InnovationInfosys TopazMastercard Pune Tech Hub

See recent placement records →

How Archer Infotech Compares

We compare ourselves against typical Pune ChatGPT / LLM training institutes on factual rows only.

Factor	Archer Infotech	Typical Pune institute
Trainer named with photo and LinkedIn	Yes — Vinod Patil	No — generic branding
Models covered	GPT-5 + GPT-4.1 + Assistants API + Realtime + Batch API	GPT-3.5-turbo only
Function calling depth	Full week — schema design, parallel calling, error handling	Basic mention
Fine-tuning coverage	GPT-4o-mini fine-tuning + RFT, capstone-eligible	Not covered
Production engineering	FastAPI streaming + Langfuse + cost dashboards	Notebook only
Public GitHub portfolio output	Yes — deployed demos with evaluation reports	Notebooks only
Salary data shown	Cited from Indeed Pune + AmbitionBox + Glassdoor + 6figr	Single number with no source
Course fee transparency	₹20,000 – ₹90,000 published	Hidden behind enquiry form
Placement support	6 months, with free re-entry	1–3 months or vague
Batch size cap	15 students	25–40 students

Compare with whoever you are considering.

ChatGPT & LLMs vs Generative AI — Which Should You Pick?

ChatGPT & LLMs is the OpenAI-ecosystem-focused 2-month course — depth on GPT-5 / GPT-4.1, Assistants API, Custom GPTs, Realtime API, Batch API, OpenAI fine-tuning. Generative AI is the broader 3-month course — Claude / GPT / Gemini all hands-on, plus open-source LLMs (Llama / Mistral) and LoRA fine-tuning, plus multi-modal.

Pick ChatGPT & LLMs if you specifically need OpenAI ecosystem depth (your team is OpenAI-only, your target employer ships on GPT). Pick Generative AI if you want broader multi-vendor / open-source / multi-modal AI engineering. Many of our students do both as a sequence.

Prerequisites & How to Start

Prerequisites: Python fluency, comfort with REST APIs and JSON, basic backend or web development. If you have done our Python or Generative AI course, you are ready.

Decide your mode — classroom, online live, or weekend
Check the upcoming batch dates
Book a free 30-minute counselling call
Confirm enrolment and complete pre-course orientation (OpenAI account creation guide)
Show up to day one with a laptop running 64-bit OS and a credit card for OpenAI API setup

Frequently Asked Questions

How long does ChatGPT & LLMs training in Pune take at Archer Infotech?+

Approximately 2 months — 8 weeks of structured curriculum. The weekend batch stretches over ~3.5 months at the same content depth.

What is the salary impact?+

AI Engineers with demonstrable OpenAI integration earn at the top of Pune's IT corridor — Indeed Pune ₹9.89 lakh average for AI Engineer; Senior AI Engineers earn ₹28–50 lakh nationally with Pune trending within ±10%.

ChatGPT & LLMs or Generative AI — which?+

ChatGPT & LLMs for OpenAI ecosystem depth. Generative AI for broader multi-vendor / open-source / multi-modal coverage. Many students do both as a sequence.

Do I need Python?+

Yes — Python fluency is required from day 1.

Is fine-tuning covered?+

Yes — week 6–7 covers OpenAI fine-tuning API for GPT-4o-mini plus reinforcement fine-tuning (RFT). Capstone Project #3 is a complete fine-tuning workflow.

Are weekend ChatGPT & LLMs classes available in Pune?+

Yes — Saturday and Sunday, 09:00–13:00, stretched over ~3.5 months instead of 2.

What is the fee?+

Course fees range ₹20,000 – ₹90,000 depending on mode. OpenAI API spend (~₹1,500) is paid by the student directly.

What support do I get after course completion?+

Six months of placement support, referrals via our alumni network at 12+ partner companies, mock interviews, salary negotiation.

Is the named trainer actually teaching?+

Vinod Patil personally leads every session of every batch.

Taught by an Industry Expert

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

Vinod Patil

Solutions Architect & AI Trainer

12 years experience