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.
• 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, 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 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.

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, a minimum of three days per week from the office, with individual flexibility.