Lead the design, architecture, and delivery of enterprise-grade GenAI and Agentic AI solutions using LLMs, RAG, AI agents, and modern cloud/MLOps platforms for our client.
Key Responsibilities
- Architect and deliver scalable LLM, RAG, Agentic AI and Multi-Agent solutions.
- Define AI architecture, governance, security, and best practices.
- Collaborate with business, product, and engineering teams.
- Mentor teams and drive AI innovation and transformation.
- Evaluate emerging AI technologies and recommend adoption.
Technical Skills
- Strong AI/ML solution architecture and Python development.
- GenAI, LLMs, RAG, embeddings, prompt engineering and fine-tuning.
- AI agents, Multi-Agent systems, tool/function calling and MCP.
- LangChain, LlamaIndex, Semantic Kernel, LangGraph, CrewAI, AutoGen or similar.
- Vector databases: Pinecone, Qdrant, Weaviate, ChromaDB, FAISS or Azure AI Search.
- Azure OpenAI, AWS AI/ML, Google Vertex AI or equivalent.
- MLOps/LLMOps using MLflow, Kubeflow, Databricks, Azure ML, etc.
- APIs, microservices, Docker, Kubernetes, SQL/NoSQL and cloud-native architecture.
- AI monitoring, evaluation, observability and governance.
Preferred
- GraphRAG, Knowledge Graphs and Enterprise Search.
- Responsible AI, compliance and governance.
- Customer-facing AI transformation experience.
You may also share your resume with us at [email protected]