Gen AI Data Architect

• Define and own enterprise-scale GenAI/AI architectures.
• Design reference architectures, reusable patterns, and integration best practices.
• Hands on Agentic AI, AI Loops, Agent loop, Strands, Agent skills, MCP,A2A, Hugging Face, RAG, LLM tunning, LangChain
• Collaborate with domain leads, data scientists, security, and developers to map requirements to scalable AI architectures.
• Evaluate LLMs, vector databases, and orchestration frameworks for performance, compliance, and cost.
• Architect RAG pipelines, agentic workflows, and multi-agent production deployments.
• Embed security, privacy, governance, and Responsible AI by design.
• Drive adoption of cloud-native AI services (Azure OpenAI, AWS Bedrock) with scalability in mind.
• Lead model lifecycle management (deployment, monitoring, retraining, drift handling) and MLOps practices.
• Recommend tools and protocols (e.g., MCP, LangChain, LangGraph, HuggingFace) for robust interoperability.

The skills you bring:

• Proven AI/ML/GenAI architecture/design experience for large-scale systems.
• Hands on Agentic AI, AI Loops, Agent loop, Strands,Agent skills, MCP,A2A, Hugging Face, RAG, LLM tunning, LangChain
• Strong Python expertise; familiarity with ML/DL frameworks (PyTorch, TensorFlow).
• Deep knowledge of LLM architectures, fine-tuning (LoRA, PEFT, adapters), and deployment strategies.
• Experience with RAG pipelines, embeddings, and vector databases (Elastic, Pinecone, Milvus).
• Familiarity with agentic GenAI systems (LangChain, LlamaIndex, AutoGen, Crew.ai, LangGraph) and MCP.
• Cloud-native architecture (AWS, Azure) and container orchestration (Docker/Kubernetes).
• Solid MLOps understanding (CI/CD for ML, observability, retraining, governance).
  • Strong business-technical communication and ability to translate requirements into practical roadmaps.