Generative AI Courses in Pune — ChatGPT, LLMs, RAG, LangChain and Prompt Engineering
Build production AI applications with LLMs, RAG and modern AI tooling at Archer Infotech's Pune institute — Generative AI, ChatGPT/Claude integration, Prompt Engineering, AI Engineer roadmap.
Build with generative AI, LLMs, and modern AI tools
Overview
AI & GenAI at Archer Infotech, Pune
Generative AI has moved from research buzz to actual production hiring in roughly 18 months. By 2026 every Pune product company of meaningful scale is shipping at least one LLM-backed feature, and several services majors have built dedicated GenAI practices to staff client engagements. Archer Infotech's Generative AI courses in Pune are built for that production-hiring reality: foundations of how LLMs work, hands-on integration with the major model APIs (OpenAI, Anthropic, Gemini), retrieval-augmented generation (RAG) with vector databases, agent frameworks like LangChain, and prompt-engineering patterns that hold up under real production constraints.
The Pune hiring landscape for GenAI roles in 2026 splits cleanly into three tracks. AI Engineer roles — building LLM-backed product features — sit in the ₹5–10 LPA fresher band at product startups and the ₹8–14 LPA range at GCC captives. Prompt Engineer roles are a real but smaller slice of the market, mostly absorbed into AI Engineer and product-facing engineering roles rather than standalone titles. AI / ML solution-architect roles for senior engineers run ₹18–30 LPA at product companies. The trap to avoid is positioning as a "prompt engineer" with no programming foundation; the high-paying GenAI roles all require working code in Python, JavaScript or both.
Archer Infotech's GenAI tracks are anchored on Vinod Patil — 12 years across solution-architect and AI-platform roles — who teaches the AI / Generative AI / Solution Architecture courses end-to-end. The Generative AI flagship covers LLM internals (transformers, attention, tokenisation), API integration (OpenAI, Anthropic, Gemini), prompt-engineering patterns, RAG with vector databases (Pinecone, Chroma, Weaviate), agent frameworks (LangChain, LlamaIndex), evaluation and guardrails. ChatGPT & LLMs is a tighter introductory track. Prompt Engineering is a 6-week focused course for product managers, content teams and developers who need to build prompt libraries without going deep on the rest of the stack. AI Tools is a 3-month survey for non-engineering roles wanting fluency across the modern AI tooling.
GenAI classes at the Kothrud institute run weekday, weekend and live online formats — weekend is by far the most popular because the GenAI student profile is overwhelmingly working developers upskilling. Every track is project-led: by week 4 you'll have a deployed LLM-backed application running against real model APIs; by course-end a portfolio of 2–3 production-grade GenAI apps with public GitHub repos. The curriculum was last reviewed 2026-05-06 against the current model versions (GPT-5, Claude Opus 4.6, Gemini 2.x), pricing tiers, and the framework versions Pune product companies actually deploy. Lifetime LMS access keeps recordings and lab walkthroughs available — important given how fast the GenAI tooling layer evolves.
Career outcomes for GenAI roles consistently sit in the upper salary bands. AI Engineer freshers with strong portfolios regularly draw ₹5–8 LPA at product startups (placement-team data, last 12 months); top performers with deployed LLM applications and benchmark experience have crossed ₹14 LPA. Working developers (2–3 years' experience) switching into AI Engineer roles routinely move from ₹8–10 LPA into the ₹15–22 LPA band. Placement support is bundled into every GenAI course fee — resume rewrite emphasising deployed AI applications, portfolio review, mock interviews calibrated to AI-engineer interview format (system design + LLM-specific evaluation rounds), and direct referrals to the 100+ hiring partners with active AI / GenAI hiring.
AI & GenAI Courses (4)
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Where AI & GenAI courses lead at Pune IT companies
Typical roles Archer Infotech alumni take after completing a AI & GenAI programme, with fresher salary bands from placement-team data (last 12 months of offers). Actual offers depend on role, company tier, and prior experience.
- Role
AI Engineer
Build LLM-backed product features — RAG, agents, prompt pipelines. Highest-demand GenAI role at Pune product startups.
5–10 LPA - Role
Prompt Engineer
Specialised role at AI-first product companies. Mostly absorbed into broader AI Engineer titles in 2026.
6–12 LPA - Role
ML Engineer with GenAI focus
Production ML deployment + LLM integration. Requires both ML pipeline experience and GenAI tooling fluency.
6–12 LPA - Role
AI Solutions Architect
Senior role designing LLM-backed systems for clients. Requires 3+ years of production AI experience.
18–30 LPA (mid-career) - Role
AI Product / Tooling Engineer
Non-LLM-core role at AI-adjacent product companies — observability, evaluation harnesses, tooling.
6–10 LPA
AI & GenAI courses — Frequently Asked Questions
The most-asked questions about Archer Infotech's ai & genai courses — choosing the right track, prerequisites, online vs offline, fees, and placement support.
Do I need a Machine Learning background for the Generative AI course?
No — the flagship GenAI track is designed for working developers, not ML researchers. You need solid Python (or JavaScript) and comfort with REST APIs; the course covers everything from there. Learners with ML background pick up the model-internals modules faster but the practical AI Engineer pattern doesn't require deep ML theory.
Which AI / GenAI course should I pick?
AI Engineer (6 months) is the right pick if you target AI Engineer roles — it covers LLMs, RAG, agents, deployment end-to-end. Generative AI (4 months) is the broader survey including ChatGPT/Claude, LangChain and prompt engineering. Prompt Engineering (6 weeks) is for non-engineering roles building prompt libraries. ChatGPT & LLMs (8 weeks) is a focused introduction. Counsellors help match background + target role during the free demo.
Will I build real AI applications during the course?
Yes — every Archer Infotech GenAI track is project-led against real model APIs (OpenAI, Anthropic, Gemini). By week 4 of any flagship course you'll have a deployed LLM-backed application; by course-end a portfolio of 2–3 production-grade GenAI apps with public GitHub repos that recruiters ask for during interviews.
Are the API costs included in the course fee?
Free-tier and trial credits cover most labs. The institute provides paid API credits for advanced labs that exceed free-tier limits — typical learner spend on personal API usage during the course is under ₹1,000. Specific spend depends on which models you experiment with for capstone projects.
How realistic are the ₹15+ LPA fresher salaries you see online?
Realistic AI Engineer fresher packages with strong portfolios run ₹5–10 LPA at Pune product startups. ₹15+ LPA fresher offers exist but are concentrated at top-tier product companies and require deployed LLM applications + benchmark experience — a small slice of the fresher market, not the median. Working developers (2–3 years' experience) switching into AI Engineer roles routinely cross ₹15 LPA. Source: placement-team data, last 12 months.
Is placement assistance included for GenAI / AI Engineer roles?
Yes. AI-role-specific placement support — resume positioning emphasising deployed AI applications, portfolio review, and mock interviews calibrated to AI Engineer interview format (system design rounds + LLM-specific evaluation rounds + product-thinking questions) — is bundled into every GenAI course fee. Direct referrals to the 100+ hiring partners with active AI / GenAI roles.