AI Architect – Lilly Medicine Foundry (r5-6)

Eli Lilly Eli Lilly · Pharma · Hyderabad, India

The role focuses on defining and implementing the technical architecture strategy for AI solutions within the Lilly Medicine Foundry. This includes designing foundational platforms, standards, and architectural patterns for AI/ML systems, MLOps infrastructure, model lifecycle management, and generative AI capabilities like RAG and agentic systems. The role also emphasizes technical standards, governance, and compliance within the pharmaceutical industry context.

What you'd actually do

  1. Define and evolve the enterprise AI architecture vision, strategy, and roadmap aligned with the Lilly Medicine Foundry and PRD objectives
  2. Design reference architectures and architectural patterns for AI/ML systems spanning model development, deployment, monitoring, and lifecycle management
  3. Design and build the core AI platform capabilities including MLOps infrastructure, model registry, feature stores, experimentation frameworks, and deployment pipelines
  4. Architect solutions for AI model lifecycle management including versioning, A/B testing, monitoring, retraining, and retirement
  5. Design enterprise-grade architectures for Large Language Model (LLM) deployment including prompt management, retrieval-augmented generation (RAG), and fine-tuning pipelines

Skills

Required

  • AI/ML systems expertise
  • Strategic architecture leadership
  • Enterprise-grade AI capabilities
  • Scalable AI solutions
  • Secure AI solutions
  • Compliant AI solutions
  • MLOps infrastructure
  • Model lifecycle management
  • Generative AI architectures
  • LLM deployment
  • RAG
  • Fine-tuning
  • Vector databases
  • Semantic search
  • Multimodal models
  • Agentic systems
  • Reasoning frameworks
  • Hybrid cloud solutions
  • Pharmaceutical regulations
  • Technical standards
  • Governance frameworks

Nice to have

  • AWS
  • Azure
  • GCP

What the JD emphasized

  • architectural patterns that ensure AI solutions are scalable, secure, maintainable, and compliant with pharmaceutical regulations
  • hybrid cloud solutions optimizing for performance, cost, and regulatory requirements
  • responsible GenAI deployment including content filtering, bias detection, hallucination mitigation, and audit trails
  • AI solution assessment frameworks to evaluate technical maturity, scalability readiness, and production worthiness

Other signals

  • design and build core AI platform capabilities
  • architect solutions for AI model lifecycle management
  • architect solutions for domain-specific AI agents
  • design vector databases and semantic search capabilities
  • evaluate and architect solutions for frontier AI capabilities