MD IDS - Product Delivery - Lead AI Data Engineer

Overview

Lead the data foundation and modernization of eCTS, a clinical supply product, by owning database engineering, migration, data products, and AI-enabled full-stack flows in a regulated pharmaceutical environment. Drive agentic AI solutions independently while setting technical direction and remaining hands-on with database design, stored procedures, performance, security, and operations.

What you'll do

• Own, maintain, and continuously improve eCTS stored procedures and database objects while converting legacy flows into full-stack and AI-enabled flows using AI-assisted approaches.
• Document embedded business rules, build automated unit and regression tests, and create a safety net for database and data logic changes.
• Design, develop, and evolve relational and non-relational data models supporting eCTS and IWRS.
• Write, review, and optimize complex SQL, stored procedures, functions, and scripts.
• Translate product and engineering requirements into robust, scalable database designs.
• Establish and enforce data modeling standards, naming conventions, and design patterns across the team.
• Lead technical execution of eCTS database modernization from legacy databases to a modern, cloud-native data platform.
• Design and deliver schema conversion, data mapping, ETL/ELT, and cutover approaches that minimize disruption to global trial operations.
• Apply AI-assisted tooling to schema conversion, code translation, and data reconciliation, with evaluation and human review.
• Design decoupled architectures using event-driven and change-data-capture patterns.
• Partner with architects to evaluate target database technologies, trade-offs, and integration patterns.
• De-risk migration through prototypes, dry runs, reconciliation, and rollback planning.
• Design eCTS data as reusable data products with ownership, documentation, data contracts, and SLAs.
• Build metadata, lineage, and semantic context for people and AI systems to find, understand, and trust eCTS data.
• Expose governed data access through APIs and agent tooling, including Model Context Protocol servers, with least-privilege and auditable access patterns.
• Apply retrieval and vector search patterns where they add value to search, support, and decision-making.
• Lead and drive agentic AI exploration and delivery across eCTS independently and hands-on.
• Build agent evaluation, guardrail, and observability practices, including test sets, quality metrics, human-in-the-loop approval, and full behavior traces.
• Challenge, influence, and align platform-level teams to advance agentic initiatives.
• Turn ideas and proofs of concept into working, production-ready capabilities.
• Act as the technical point of reference for eCTS database and data platform matters.
• Guide engineers on data design, performance tuning, best practices, and effective use of AI coding and data tools.
• Review database changes and pull requests for quality, consistency, and maintainability.
• Deliver version-controlled schema and logic, automated migrations, CI/CD database changes, quality gates, infrastructure as code, and repeatable environments.
• Monitor, diagnose, and tune database performance, including indexing, query plans, partitioning, and capacity.
• Ensure high availability, backup/recovery, and disaster-recovery readiness.
• Define service-level objectives and data observability, resolve production data issues, and prevent recurrence.
• Build AI-enabled monitoring and operational tooling, including anomaly detection and assisted root-cause analysis.
• Ensure database solutions meet GxP, ALCOA+, validation, audit, CSA, ICH E6(R3), security, access-control, encryption, and data-retention requirements.
• Define validation, review, and recording practices for AI-assisted changes and agent actions.
• Maintain documentation and traceability for validated, regulated systems.
• Partner with the eCTS Product Manager, engineers, architects, and quality teams throughout delivery.
• Contribute to backlog refinement, estimation, sprint delivery, and incremental release-based delivery in an Agile environment.
• Coach and mentor engineers on database design, performance, modernization, and AI-enabled engineering practices.
• Share domain and technical knowledge and prioritize shared team success.

What you'll need

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical discipline.
• 6+ years of hands-on database engineering / development experience.
• Experience leading database design and development on non-trivial software products.
• Experience delivering data migration or database modernization efforts.
• Experience working with engineering teams in Agile environments.
• Strong SQL and relational database expertise, including performance tuning and query optimization, with SQL Server, Oracle, PostgreSQL, or equivalent.
• Deep, hands-on comfort with stored procedures and complex database logic, and a genuine willingness to own and improve long-standing legacy objects.
• Solid data modeling skills across transactional and analytical use cases.
• Experience with ETL/ELT and data migration tooling and patterns.
• Working knowledge of cloud data platforms, Azure and/or AWS, and modern managed database services.
• Understanding of database security, high availability, backup/recovery, and monitoring.
• Hands-on experience building AI- or LLM-enabled solutions, such as agents, retrieval, or AI-assisted code and data workflows, including at least one used by others beyond a personal prototype.
• Experience with database-as-code practices, including source control, CI/CD, automated migrations, and database change management.
• Ability to read and reason about application code well enough to partner effectively with engineers on data-related issues.
• Demonstrated ability to work autonomously, drive technical initiatives end to end, and influence other teams without relying on close direction.
• Comfort making sound decisions with incomplete information in a fast-moving technology space and learning quickly as tools and practices evolve.

Nice to have

• Experience in Pharmaceutical, Life Sciences, Clinical Development, Supply Chain, or Regulatory / Quality domains.
• Understanding of GxP, validation, data integrity, and regulated environments, including risk-based approaches such as Computer Software Assurance.
• Experience with clinical, manufacturing, or laboratory systems, such as IWRS / clinical supply systems.
• Experience with agent frameworks, Model Context Protocol, LLM evaluation, or AI observability tooling.
• Experience with automated database testing and migration tools, such as tSQLt, pgTAP, Flyway, or Liquibase, and infrastructure as code.
• Familiarity with event streaming and CDC, NoSQL, vector databases, lakehouse platforms such as Databricks, or analytics platforms.
• Relevant database, cloud, or AI certifications.

Details

• Location: Bengaluru, India.
• Work authorization in the country where the position is located is required for consideration.
• The role supports clinical supply systems and data in a regulated pharmaceutical environment.