GenAI Lead

We are looking for a hands-on GenAI Lead to design, build, and deploy production-grade AI solutions for enterprise use cases. The role requires strong Generative AI, Agentic AI, RAG, Python, and cloud engineering experience, along with the ability to lead technical planning and drive solutions from concept to production.

What We’re Looking For

• 10+ years of overall experience in AI/ML, Data Science, or software engineering
• Minimum 2+ years of hands-on GenAI / LLM development experience
• Strong Python development and software engineering fundamentals
• Hands-on experience building LLM-powered applications and Agentic AI systems
• Strong experience with RAG, Graph RAG, embeddings, vector databases, and hybrid search
• Experience with LangChain, LangGraph, CrewAI, AutoGen, or similar agent frameworks
• Experience working with OpenAI, Claude, Gemini, Hugging Face, or open-source LLMs
• Good understanding of prompt engineering, context engineering, function calling, and tool integration
• Knowledge of LLM evaluation, guardrails, observability, and LLMOps
• Experience deploying AI applications on AWS or Azure
• Working knowledge of Docker, Kubernetes, APIs, CI/CD, and cloud-native architectures
• Strong understanding of ML/NLP fundamentals and transformer-based models

Key Responsibilities

• Architect and develop enterprise GenAI and Agentic AI solutions
• Build RAG / Graph RAG and advanced retrieval pipelines
• Design and orchestrate multi-agent workflows with tools and function calling
• Build scalable APIs and integrate AI solutions with enterprise applications
• Evaluate and optimize LLMs for quality, latency, reliability, and cost
• Implement LLMOps, evaluation, monitoring, guardrails, and observability
• Lead technical planning and convert business requirements into practical AI solutions
• Conduct architecture and code reviews and establish engineering best practices
• Mentor engineers and provide technical guidance to the AI development team
• Communicate effectively with engineering, product, domain, and business stakeholders

Core Technology Keywords

GenAI / LLM: LLM, Generative AI, Agentic AI, Multi-Agent Systems, Prompt Engineering, Context Engineering Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Hugging Face RAG: RAG, Graph RAG, Vector Search, Hybrid Search, Embeddings, Knowledge Graphs Databases: Pinecone, Weaviate, Chroma, pgvector, PostgreSQL, Neo4j Development: Python, FastAPI, SQL, Git ML: PyTorch, TensorFlow, Scikit-learn, Transformers, LoRA/QLoRA LLMOps: LangSmith, Langfuse, RAGAS, MLflow, LLM Evaluation Cloud: AWS Bedrock, SageMaker, Lambda / Azure OpenAI DevOps: Docker, Kubernetes, CI/CD

Preferred

Experience in pharmaceutical, healthcare, or regulated industries is advantageous. Exposure to knowledge graphs, Graph RAG, LLM fine-tuning, inference optimization, and responsible AI would be an added advantage.

We are looking for someone who is accountable, self-driven, technically strong, and comfortable taking ownership of AI solutions from planning through production deployment.