Role Overview

We are looking for a 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 R3 on our engineering ladder - see the level framing below for the expected scope of ownership and impact.

Level framing: Recognized technical expert; leads decisions on technical approach for projects; solves complex problems and innovates solutions; drives improvements across multiple projects/teams; may manage budgets.

Core Responsibilities

- Design and build complex ML/AI systems and services, setting the technical approach for your workstream.
- Solve complex, ambiguous technical problems that span multiple components or teams.
- Set code-quality and engineering standards for your area, and lead by example in code review.
- Own the MLOps design for a significant system: CI/CD, orchestration (Kubernetes/Prefect), and production monitoring.
- Lead root-cause analysis for complex production incidents and drive systemic fixes, not just patches.
- Extend the teams MLOps frameworks to support new model types or deployment patterns.
- Lead design of agentic AI systems (e.g. LangGraph-based), including multi-step reasoning and RAG architecture decisions.
- Own LLMOps practices for your area: evaluation pipelines, guardrails, and cost/latency optimization.
- Evaluate and introduce new GenAI tools, frameworks, or techniques where they meaningfully improve the platform.
- Mentor other engineers and help raise technical standards within your workstream.

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

8-11 years of hands-on experience, with a track record of owning significant technical workstreams end-to-end.
- Solid 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.

for further assistance. .

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.

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