Coforge is hiring AI Engineers / AI Leads with 3+ years of experience for Greater Noida, Gurugram, Hyderabad, Pune and Bangalore. The role focuses on Generative AI, LLMs, Agentic AI and intelligent enterprise applications. Immediate joiners are specifically requested in the hiring post.
🏢 About the Company
Coforge is a global AI-native engineering services company combining AI engineering with industry expertise. The company says it has 45,000+ professionals across 33 countries and 54 global delivery centers. Its current strategy emphasizes enterprise AI, trusted AI, AI agents and production-scale solutions. Coforge has also expanded its work around agentic AI and AI operations, making this role closely aligned with its current technology direction.
🌟 Why This Role Is Good for Experienced Professionals
This is more than a conventional ML engineering position. The stack spans LLMs, RAG, vector databases, prompt engineering, agentic AI, cloud AI platforms and MLOps/LLMOps. For 3+ years professionals, it can provide exposure across the GenAI application lifecycle—from model/API selection and retrieval pipelines to agent orchestration and production optimization.
• Build enterprise-oriented GenAI and AI-agent solutions
• Work with LangChain/LangGraph and RAG architectures
• Gain exposure to Azure OpenAI, AWS Bedrock or GCP Vertex AI
• Strengthen Python, ML/DL and AI application engineering
• Develop LLMOps/MLOps and production AI skills
• AI Lead candidates can demonstrate architecture, mentoring and solution ownership
📌 Job Overview
• Company: Coforge
• Role: AI Engineer / AI Lead
• Experience: 3+ Years
• Locations: Greater Noida, Gurugram, Hyderabad, Pune, Bangalore
• Joining: Immediate joiners only, according to the supplied hiring post
• Core Areas: GenAI, LLMs, Agentic AI, Python, RAG, LangChain/LangGraph, Vector DBs, Cloud AI
🧠 Skills Required
Candidates should be strong in Python and understand machine learning and deep learning fundamentals. Practical LLM application knowledge should cover embeddings, chunking, retrieval, reranking, prompt engineering and response generation. LangChain/LangGraph experience is useful for orchestration and agent workflows. Knowledge of vector databases such as FAISS, Pinecone, Weaviate, Milvus or Azure AI Search is valuable. Candidates should know at least one of Azure OpenAI, AWS Bedrock or GCP Vertex AI. Senior/lead profiles should additionally understand architecture, evaluation, security, observability, cost optimization and MLOps/LLMOps.
🎯 Expected Coforge Interview Rounds
- Recruiter Screening – Experience, notice period, location, current AI/GenAI work and fit.
- AI/Technical Round – Python, ML/DL, LLM concepts, RAG, embeddings, vector search, prompting and agents.
- Practical/Architecture Round – Design a production GenAI solution and discuss hallucination control, evaluation, security, latency and cost.
- Lead/Managerial Round – For AI Lead candidates, expect architecture ownership, mentoring, stakeholder communication and delivery scenarios.
- HR/Final Discussion – Compensation, availability, location and employment details.
💰 Salary Range
The supplied hiring post does not mention an official CTC. Third-party data should therefore be treated only as market context. AmbitionBox reports Coforge AI/ML Engineer compensation in Pune at approximately ₹8.1–₹10.4 LPA in a 2025 low-confidence estimate. Actual compensation for this GenAI role may differ significantly based on experience, technical depth, lead responsibilities, location and negotiation.
🔥Preparation Tips
🔥 Build one end-to-end RAG project covering document ingestion, chunking, embeddings, vector storage, retrieval and grounded generation.
🤖 Practice Agentic AI with LangGraph/LangChain using tools, state, routing and error handling; know when agents are preferable to straightforward RAG.
🧠 Revise LLM fundamentals: transformers, attention, tokens, temperature, context windows, embeddings, fine-tuning vs RAG and hallucination mitigation.
☁️ Choose one cloud deeply—Azure OpenAI, Bedrock or Vertex AI—and prepare authentication, deployment, monitoring and cost considerations.
📊 Prepare an evaluation approach covering retrieval quality, groundedness, relevance, latency and response quality.
⚙️ Revise production concerns: guardrails, PII protection, rate limits, observability, caching, prompt/version management and token-cost optimization.
💻 Strengthen Python through APIs, async programming, testing, data handling and clean modular code.
📄 Resume Tips
Make the top section immediately relevant to GenAI. Mention specific models, cloud platforms, LangChain/LangGraph, RAG, vector databases and Python rather than only “AI/ML.” Quantify results such as improved retrieval accuracy, reduced response time, lower token cost or increased automation. Add a GenAI Projects section if relevant. For AI Lead applications, highlight architecture ownership, mentoring, client interaction, design reviews and production deployment. Avoid listing tools without explaining what you built with them.
📩 How to Apply
📩shriya.jain@coforge.com
Share your updated resume at shriya.jain@coforge.com, as provided in the hiring post. You can also verify opportunities through the official Coforge careers portal. Coforge warns candidates about recruitment fraud and states that it does not ask applicants for money.
Final Thoughts
This Coforge opening combines LLMs, Agentic AI, RAG, cloud AI services and Python engineering. Candidates should demonstrate working AI solutions rather than only course knowledge. With immediate joining specified, keep your resume concise, project-focused and ready to explain the architecture, trade-offs and production challenges behind every GenAI project.
