FACE TO FACE iNTERVIEW

Location: Navalur, Chennai, Tamil Nadu, India

Mode of Interview: Face-to-Face (Walk-in)

Interview Date: Saturday ( 01-August-2026)

Employment Type: Full time

Role & responsibilities
Design, develop, and deploy LLM-powered applications using Python.
Build and optimize RAG (Retrieval-Augmented Generation) pipelines for accurate, context-aware AI responses.
Develop multi-step, agentic AI workflows using LangGraph for complex task orchestration.
Build LLM application pipelines using LangChain, including prompt engineering, chaining, and tool integration.
Implement and optimize vector search and embedding-based retrieval systems.
Integrate LLMs with enterprise data sources, APIs, and knowledge bases.
Design and implement prompt engineering strategies (few-shot, chain-of-thought, etc.) for improved model performance.
Collaborate with cross-functional teams to identify GenAI use cases and translate them into technical solutions.
Ensure scalability, security, and reliability of AI-powered applications in production.




Monitor and evaluate LLM outputs for accuracy, relevance, and hallucination control.
Write clean, maintainable, and well-documented Python code.

Preferred candidate profile
Strong proficiency in Python for backend/AI application development.
Hands-on experience with LLMs (OpenAI GPT, LLaMA, Claude, or similar).
Proven experience building RAG (Retrieval-Augmented Generation) architectures.
Robust hands-on experience with LangChain for LLM application development.
Experience with LangGraph for multi-agent and stateful AI workflow orchestration.
Experience with vector databases (Pinecone, FAISS, Chroma, Weaviate, or pgvector).
Understanding of embeddings, semantic search, and chunking strategies.
Experience with prompt engineering techniques.
Familiarity with REST API development (FastAPI, Flask) for serving AI models.
Experience with cloud platforms (AWS, Azure, or GCP) for AI/ML deployment.
Solid problem-solving and analytical skills.

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