GenAi Lead / Architect

Role: Agentic AI Lead (Data Engineering → GenAI Transformation)

We are seeking senior Data Engineering leaders who have evolved into hands‑on GenAI / Agentic AI practitioners. This role is not for pure research, academic, or experimentation-focused profiles. The expectation is production-grade delivery, grounded in strong data engineering fundamentals and scaled enterprise systems.

Key Responsibilities

Lead Agentic AI Delivery at Enterprise Scale

• Lead end-to-end architecture, design, and production deployment of Agentic AI solutions for complex enterprise and Life Sciences use cases
• Build, deploy, and optimize multi-agent systems involving planning, reasoning, orchestration, tool usage, and memory management
• Drive GenAI implementations beyond POCs into stable, scalable, and observable production systems

Must-Have Profile : Core Background

• 12+ years of experience with a strong foundation in Data Engineering, evolving into AI / GenAI delivery roles
• Proven experience delivering production-grade GenAI / Agentic AI solutions in real enterprise environments

Data Engineering Excellence

• Deep expertise in Databricks (PySpark, Delta Lake, workflows, optimization)
• Extensive experience designing, building, and scaling ETL pipelines (batch and streaming)
• Strong programming skills in Python and SQL
• Hands-on experience with cloud platforms (AWS, Azure, or GCP)

Agentic AI & GenAI Capabilities

• Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent
• Real-world implementation of multi-agent systems and autonomous workflows
• Experience building RAG-based, tool-integrated AI solutions
• Practical knowledge of model fine-tuning / adaptation techniques
• Strong understanding of:
• Prompt engineering
• LLM orchestration and tool usage
• Memory handling, agent context, and workflow optimization