Sr. Principal AI Software Engineer

Role Summary

Sr. Principal AI Software Engineer responsible for leading development and deployment of transformative AI solutions across the enterprise, architecting autonomous AI systems to enhance safety, quality, and operational efficiency across functions from drug discovery to corporate operations.

Responsibilities

• Architect Production AI Systems: Design and ship scalable autonomous agent platforms that solve real problems‚Äîown the full stack from LLM integration to production deployment.
• Build on Lilly Cortex: Leverage enterprise AI platform to architect sophisticated agentic systems using Model Context Protocol for tool integration, Agent-to-Agent communication for multi-agent coordination, and dynamic UI frameworks that wrap intelligent experiences around agent workflows.
• Ship Fast with AI Tooling: Use AI development tools and LLM-driven workflows to 10x output; set standards for modern engineering teams building with AI.
• Define the AI Platform Strategy: Own the technical vision for Studio as the company‚Äôs AI business platform; make architectural decisions at scale from prototype to enterprise.
• Level Up the Team: Mentor engineers on modern AI/ML practices, prompt engineering, and agentic patterns; foster a high-trust, high-velocity culture.
• Establish Engineering Standards: Create CI/CD for AI systems, evaluation frameworks, observability for agent workflows, and compliance patterns for regulated environments.
• Drive Product Impact: Partner with product, operations, and business teams to identify high-leverage AI opportunities and ship features that move metrics.
• Stay on the Edge: Evaluate new models, test emerging frameworks, and bring latest research into production.

Qualifications

• Basic Requirements: Bachelor's, Master's, or PhD in Computer Science, Engineering, or related field
• 8+ years of software engineering experience
• 2+ years architecting and deploying production AI systems
• Proven success delivering AI solutions in regulated or enterprise-wide environments

Skills

• Core Technical Expertise:
  • AI Development Proficiency: Experience building production applications with AI-powered development tools (GitHub Copilot, Cursor, Claude, ChatGPT, Grok)
  • Agent Architecture: Designing and deploying autonomous agent systems, including orchestration patterns, tool selection, and failure handling
  • System Integration: Model Context Protocols, API integrations, and extensible AI architectures
  • Modern AI Stack: Python, LangChain/LangGraph, vector databases (Pinecone, Weaviate, ChromaDB), embedding models, and advanced prompt engineering
• Leadership & Communication:
  • Experience leading technical teams through AI transformation
  • Ability to communicate complex technical concepts to diverse audiences
  • Mentoring engineers and fostering innovation
  • Strategic mindset balancing technical excellence with business outcomes
• Infrastructure & MLOps:
  • End-to-end MLOps (model versioning, monitoring, CI/CD for ML)
  • Cloud AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
  • Container orchestration (Kubernetes) and scalable deployment architectures
  • AI system observability, performance tuning, and cost optimization

Education