Technical Lead Software Architect

Eli Lilly Eli Lilly · Pharma · Hyderabad, India

Technical Lead Software Architect for the AI & Platform team at Eli Lilly, focusing on designing, building, and scaling intelligent systems using Generative AI and LLMs. The role involves owning end-to-end delivery of GenAI/LLM platform capabilities, designing multi-agent AI architectures, and establishing engineering best practices for cloud-native solutions. Requires expertise in backend and frontend development, agentic AI frameworks, containerization, and cloud platforms.

What you'd actually do

  1. Architect and lead the delivery of scalable, cloud-native software systems across backend and frontend layers.
  2. Own end-to-end design and implementation of GenAl and LLM-powered platform capabilities in production.
  3. Design and govern multi-agent Al architectures using MCP, A2A, and emerging agentic frameworks.
  4. Define containerisation, orchestration, and deployment standards using Docker, Kubernetes, and Helm.
  5. Lead architectural reviews, establish engineering best practices, and set technical direction for the team.

Skills

Required

  • Python
  • Go
  • TypeScript
  • Generative AI
  • LLM
  • RAG
  • LLM evaluation frameworks
  • object-oriented design principles
  • advanced design patterns
  • multi-service systems
  • agentic AI systems
  • MCP
  • A2A
  • multi-agent orchestration frameworks
  • Docker
  • containerised development
  • AWS
  • Azure
  • GCP
  • SQL
  • NoSQL databases
  • schema design
  • query optimisation
  • indexing strategies
  • data modelling
  • problem-solving
  • systems thinking

Nice to have

  • React
  • Next.js
  • FastAPI
  • microservices
  • distributed system design
  • service mesh
  • API gateway patterns
  • event-driven architecture
  • inter-service communication standards
  • BDD
  • TDD
  • contract testing
  • test automation frameworks
  • authentication
  • authorisation
  • Auth 2.0
  • JWT
  • SAML
  • OIDC
  • Kubernetes
  • Helm chart authorship
  • GitOps
  • Agile/Scrum
  • GitHub Copilot
  • claude code
  • prompt engineering

What the JD emphasized

  • Expert-level proficiency in at least one programming language — Python, Go, or TypeScript — with a demonstrated track record of building and owning production-grade systems at scale.
  • Proven experience architecting and delivering Generative Al and LLM-based solutions end-to-end, including model integration, prompt pipelines, RAG systems, and LLM evaluation frameworks.
  • Hands-on experience designing and implementing agentic Al systems using MCP, A2A, or equivalent multi-agent orchestration frameworks beyond familiarity, with the ability to define architectural patterns for the team.

Other signals

  • design, build, and scale intelligent systems
  • Generative AI and Large Language Models
  • agentic AI, model-context protocols
  • GenAl and LLM-powered platform capabilities
  • multi-agent Al architectures
  • Generative Al and LLM-based solutions
  • agentic Al systems