Job Summary

We are looking for a Senior Principal Machine Learning Engineer to join the AI Engineering team, with a primary focus on Hands-On Engineering (MLE) and MLOps & Platform Reliability and GenAI & Agentic Systems. This posting is at level R4 on our engineering ladder - see the level framing below for the expected scope of ownership and impact. Level framing: Recognized expert who mentors others; makes key technical decisions impacting multiple teams; leads resolution of highly complex challenges; cross-functional influence; may engage with external partners as an authority.

Core Responsibilities

- Design and build production backend systems for agentic AI applications - API layers, backend-for-frontend (BFF) patterns, session/state management, and streaming for concurrent multi-user workloads.
- Lead resolution of highly complex, cross-system technical challenges.
- Set the technical bar for hands-on engineering practice across the team.
- Own GitOps and engineering-governance standards across the team (version control, CI/CD, review, promotion).
- Make key MLOps/platform decisions that affect multiple teams (e.g. deployment topology, observability strategy).
- Drive triage and resolution of complex, cross-system production incidents.
- Own architecture for agentic AI systems, including routing, policy enforcement, and RAG/knowledge-registry design.
- Lead evaluation and adoption of recent agent frameworks, LLMOps practices, and GenAI tooling across the team.
- Implement authentication/authorization flows for agentic systems (OAuth/OIDC, on-behalf-of token exchange, secure credential storage).
- Mentor other engineers on architecture, design patterns, and production engineering practices,



and act as a role model for engineering rigor across the wider team.

Key Tools & Technologies

- Cloud & Data Infra: AWS (EC2/ECS, S3, Lambda, IAM, CloudWatch or equivalent); Databricks & Unity Catalog
- Foundational: Python; Git/GitHub; software engineering best practices; testing; SQL
- MLOps & Deployment: Docker, Kubernetes, Prefect; CI/CD (GitHub Actions); production monitoring; model versioning & lineage; GitOps governance
- GenAI & Agentic Architecture: Claude or comparable LLMs; LangGraph or comparable agent frameworks; RAG architectures; vector databases (e.g. Pinecone); prompt engineering & evaluation

Required Qualifications

11-15 years of hands-on experience, with demonstrated growth into architecture-level ownership spanning multiple teams or systems.
- Strong proficiency in Python and a track record of writing clean, testable, production-quality code.
- Demonstrated experience owning CI/CD, containerisation, and orchestration for production ML/AI systems.
- Proven experience developing or deploying LLM-based applications, including prompt engineering, RAG, or agentic workflows.
- Strong working knowledge of containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines.
- Working knowledge of AWS cloud services and Databricks/Unity Catalog or equivalent enterprise data platforms.
- Excellent verbal and written communication skills.
- Experience working in Agile/Scrum environments.

Education

Bachelors or Masters degree in Computer Science, Computer Applications, or a related technical field. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Principal Machine Learning Engineer (Bengaluru)
🏢 Eli Lilly
📍 Bengaluru