Position Summary
We are seeking
senior Data Engineering leaders who have evolved into handson 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.
Job 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
Deep Integration with Enterprise Data Platforms
- Architect and integrate Agentic AI systems with Databricks, data lakes, data warehouses, streaming platforms, and enterprise APIs
- Design and optimize scalable ETL / ELT pipelines (batch and streaming) to power AI, ML, and GenAI workflows
- Ensure data quality, lineage, freshness, and governance for AI-driven applications
AI Architecture, Optimization & Governance
- Define architecture patterns, guardrails, and governance frameworks for enterprise Agentic AI
- Optimize agent workflows through prompt engineering, tool selection, orchestration strategies, and memory design
- Define approaches for context management, token efficiency, latency optimization, and cost control
- Ensure reliability, observability, security, and performance of AI systems in production
Leadership & Stakeholder Engagement
- Partner with business stakeholders to identify high-impact AI use cases and translate them into scalable solutions
- Mentor and lead cross-functional teams across Data Engineering, AI/ML, and Application Engineering
- Participate in client discussions, roadmap definition, solutioning, proposals, and Agentic AI thought leadership
Education
BE/B.Tech
Master of Computer Application
Work Experience
Core Background (NonNegotiable)
- 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 (Candidates limited to academic, research, or POC-only experience are not suitable)
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
Skills That Give You An Edge
- Experience with enterprise-scale AI transformations, preferably in Life Sciences / Pharma
- Exposure to LLMOps / MLOps (monitoring, evaluation, governance, drift detection)
- Strong understanding of AI evaluation, guardrails, and Responsible AI practices
- Ability to translate business problems into scalable, governed AI solutions
- Experience operating AI systems with cost, performance, and reliability SLAs
Behavioural Competencies
Teamwork & Leadership
Motivation to Learn and Grow
Ownership
Cultural Fit
Technical Competencies
Problem Solving
Lifescience Knowledge
Communication
Project Management
Capability Building / Thought Leadership
Databricks
Python
PySpark
