Director - Principal Data Engineering Lead

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About the role

Job Title: Director - Principal Data Engineering Lead GCL: F Location: Chennai   Introduction to role: Are you ready to turn the latest AI breakthroughs into resilient, production-grade systems that accelerate late-stage scientific discovery and move life-changing medicines closer to patients? Can you picture yourself setting enterprise-wide Data and AI strategy while building reusable autonomous frameworks and GenAI capabilities that scale with confidence? In this role, you will lead how we embed intelligence into the digital backbone of drug development. You will develop long-term platform funding priorities, capability roadmaps, and technology choices. Then you will translate strategy into solutions that make complex enterprise workflows simpler, safer, and faster for our scientists and product teams. Join us at a pivotal point in our transformation into a digital and data-led enterprise. Backed by investment and an ecosystem of partners, you will work across AI science, engineering and business partners to create patterns that become the standard and prove them through high-impact deliveries that stand up in a regulated environment.   Accountabilities: • Shape and evolve the long-term Data and AI strategy for drug development, including platform investments, capability roadmaps, technology choices and alignment with enterprise architecture. • Partner with AI science teams to design reusable agentic patterns, GenAI components and scalable solutions that streamline sophisticated enterprise workflows and accelerate delivery. • Influence enterprise architecture decisions and lead strategic vendor evaluations, pilots and ecosystem partnerships to bring the best technologies into the enterprise. • Lead multi-functional engineering teams, driving end-to-end delivery from prototype to robust, compliant production systems. • Define, govern and promote engineering standards, guidelines and reusable design patterns; produce and review detailed technical designs across data models, APIs, integrations, security, deployment, error handling, performance, monitoring, testing and support. • Promote strong data integrity and governance, including clear ownership, quality management, metadata, lineage, audit trails, master and reference data, and standardized terminology. • Establish and maintain AI and ML governance controls, covering risk assessment, proper use, human oversight, explainability, model approval, versioning, performance monitoring, change control and retirement. • Implement production-grade controls for GenAI and retrieval-augmented generation solutions to deliver reliable, traceable and secure outputs based on trusted information, including prompt and model management, response quality testing, source referencing, access controls and mitigations for prompt attacks and data leakage. • Define and operate comprehensive evaluation and monitoring frameworks that track accuracy, reliability, security, response quality, business relevance, degradation, latency, cost and operational stability. • Establish API-based and event-driven integration standards, including data formats, contracts, versioning, interoperability and integration with legacy and regulated systems. • Improve value from enterprise data platforms and guide the implementation of DevOps, DataOps and MLOps practices to deliver resilient, automated, auditable pipelines across data, ML and AI. • Ensure data solutions adhere to FAIR principles—findable, accessible, interoperable and reusable—and support end-to-end machine learning and AI workflows. • Foster a culture of continuous improvement, experimentation, collaboration, and innovation across engineering and AI teams, encouraging thoughtful use of AI and digital tools in day-to-day work.   Essential Skills/E

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