Gen AI Lead/Architect

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

Role Intent (New – Critical)

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 experimentationfocused 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

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

Must-Have Profile (Repositioned Clearly)

Core Background (Non-Negotiable)

• 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:

o Prompt engineering

o LLM orchestration and tool usage

o 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.