Generative AI Engineer

Key Responsibilities:

• Lead the end-to-end design and development of Generative AI applications using LLMs.
• Architect intelligent systems using LangChain / LangGraph for multi-agent workflows, planning, task execution, verification, and orchestration.
• Implement RAG pipelines using embeddings, hybrid search, semantic indexing, and reranking.
• Design and optimize Vector DB structures (FAISS, Qdrant, Pinecone, Weaviate, Chroma) for high-performance retrieval.
• Create knowledge ingestion pipelines including chunking, preprocessing, OCR and document embedding strategies.
• Build scalable backend services using Python / FastAPI / Node integrated with LLMs (OpenAI, Anthropic, Gemini, Llama).
• Own POCs, architecture decisions, prompt engineering frameworks, and performance tuning.
• Drive experimentation of LLM models, fine-tuning, LoRA, embedding selection and evaluation metrics.
• Collaborate with cloud and product teams to deploy scalable GenAI solutions on AWS / GCP / Azure.
• Mentor and guide engineers, review code, and set best practices for model usage & observability.
• Stay current with research trends in GenAI, multi-agent systems and knowledge reasoning.

Required Skills & Expertise

• 510+ years overall development experience, 1-2+ years in Generative AI .
• Strong expertise in LangChain & LangGraph for orchestration, agents, tools and memory management.
• Hands-on experience with Vector Databases (Qdrant / Pinecone / FAISS / Chroma / Weaviate).
• Deep understanding of LLM architecture , embeddings, RAG, token optimization & context strategies.
• Experience with Multi-Agent Frameworks (Planner, Worker, Verifier, Summarizer).
• Strong programming experience in Python (preferred) or NodeJS.
• Familiarity with LLM model providers OpenAI, Anthropic, Gemini, HuggingFace, Meta Llama.
• Experience with cloud-native deployment Docker, Kubernetes, CI/CD .
• Understanding of prompt engineering, safety guardrails, hallucination prevention & evaluation.
• Ability to lead projects, conduct research, run experiments and deliver production-ready systems.