Team Lead Engineering AI

Overview

Lead and build a high-performing AI Engineering team in Biometrics and Clinical Evidence (BCE), translating scientific and business opportunities into trusted, scalable and reusable AI and agentic solutions from proof of concept through production.

What you'll do

• Build, develop and lead a high-performing team of AI Engineers, establishing a strong engineering culture focused on technical excellence, experimentation, reuse, collaboration and measurable impact.
• Define the capabilities, roles, development pathways and engineering practices required for AI Engineering across BCE and the broader R&D RDT Org.
• Partner closely with AI Scientists, Product Managers, data teams and PD stakeholders to translate high-value business and scientific problems into technically robust AI solutions.
• Provide technical leadership from early exploration and proof of concept through architecture, development, deployment, adoption and lifecycle management.
• Ensure rapid experimentation can transition effectively into production-grade solutions where value is demonstrated.
• Define and implement reusable patterns, components and reference architectures for generative AI, retrieval-augmented generation, agentic workflows, knowledge retrieval and AI-enabled applications.
• Drive a "build once, reuse many" engineering philosophy, reducing duplication and accelerating delivery across use cases.
• Identify common capabilities emerging across individual solutions and partner with platform teams to industrialise them as shared services where appropriate.
• Architect scalable data pipelines, high-throughput vector search indexes, and hybrid retrieval systems tuned for LLM grounding and retrieval-augmented generation.
• Define and implement structured schema metadata formats and function-calling protocols to ensure precise, reliable agent tool invocation.
• Optimize dynamic context windows, chunking strategies, and data freshness to drive accurate model reasoning across complex enterprise domains.
• Own the AI full stack, from front-end/UX and back-end services through data and context provisioning to AI engines and agentic workflows, making key decisions on tech stack, semantic/knowledge graph and context layers, and performance optimization.
• Identify and develop future-focused team capabilities and mentor team members on long-term career paths.
• Address complex systemic roadblocks affecting multiple development teams and coach other managers on effective performance management.
• Establish and monitor metrics for team health and sustainability across several development teams, and design initiatives to improve collaboration and psychological safety.
• Manage talent composition for own and related teams, planning future needs and a healthy skills pipeline.

What you'll need

• 8–12+ years of relevant technology, software or data experience, with meaningful technical leadership experience and a proven track record of building high-performing teams.
• 2+ years of experience in the pharmaceutical, biotechnology, or regulated healthcare technology industry.
• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Computational Science, Bioinformatics or a related quantitative discipline, or equivalent experience demonstrating deep technical capability.
• Strong practical understanding of modern AI engineering, including generative AI / LLMs, RAG, agentic architectures, tool use/function calling, orchestration, evaluation, prompt/context engineering, embeddings/vector search, knowledge retrieval, model selection and observability.
• Experience with fine-tuning/distillation, multimodal systems and high-throughput serving (vLLM).
• Proficiency in Python, APIs, microservices, cloud architectures, CI/CD, containers, Git, automated testing, infrastructure/platform patterns, identity/access management, observability and DevSecOps.
• Knowledge of AI FinOps (token/GPU cost management), model fallback routing and cloud-native container orchestration (Kubernetes, AWS/GCP serverless platforms).
• Experience with data pipelines, semantic models, metadata, APIs, structured/unstructured data, vector databases, knowledge graphs, interoperability, data quality, lineage and provenance.
• Understanding of privacy, security, GxP considerations, validation, auditability, traceability, human-in-the-loop, model evaluation and AI risk management.
• Experience with architecture standards, engineering quality, code review, reusable patterns, technical mentoring, hiring, capability development, prioritisation and delivery discipline.
• Strong problem framing and product thinking, scientific curiosity and value orientation.
• Demonstrated servant and creative leadership behaviour, executive presence, business acumen and ability to navigate ambiguity and complexity.
• Enterprise mindset that breaks down silos and empowers teams and individuals to act autonomously while holding them accountable.

Nice to have

• Advanced degree (preferred but not required where equivalent experience demonstrates deep technical capability).
• Vendor lock-in mitigation, buy vs. build frameworks and AI change management experience.
• Keen focus on major IT market trends in AI.

Details

• Location: Hyderabad.