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
