Director - Principal Data Engineering Lead

Overview

Lead enterprise-wide Data and AI strategy for drug development, building reusable agentic patterns and GenAI capabilities that move from prototype to compliant production systems.

What you'll do

• 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.

What you'll need

• Track record of leading end-to-end delivery of AI-native products within a controlled or enterprise environment.
• Demonstrable ability translating research advances in GenAI or agent-based systems into scalable industrial solutions.
• Deep knowledge of modern cloud and data architecture patterns: microservices, serverless, etc.
• Hands-on experience with containerization (Docker, Kubernetes), SQL/NoSQL databases, CI/CD, and infrastructure-as-code.
• Fluency in Python, with a good understanding of secure software engineering practices.
• Experience mentoring engineers and leading high-performing teams.
• Comfortable influencing senior partners and shaping technology strategy across functions.

Nice to have

• Pharma industry experience, particularly in R&D or Late-Stage Development.
• AI or Gen AI technical certifications or equivalent.
• AWS Cloud Certification or equivalent.

Details

• Location: Chennai, India.
• Work mode: in-person working, on average a minimum of three days per week from the office.