Senior Principal Machine Learning Engineer

Eli Lilly
Eli Lilly logo
Location
IN: Lilly Bengaluru
Job Type
Full-time
Posted
October 7, 2026
Views
4

Job Description

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Role Overview

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 new 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

Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field.

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Role Overview

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 new 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

Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

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Frequently Asked Questions

Where is the job located, and is it remote/hybrid/on-site?
The job is located in Bengaluru, India (IN: Lilly Bengaluru). The job posting does not specify a remote, hybrid, or on-site work-mode policy.
What are the required qualifications and experience level for this role?
You need 11–15 years of hands-on experience with growth into architecture-level ownership. Required skills include strong Python proficiency, experience with CI/CD, containerisation (Docker), orchestration (Kubernetes), AWS, Databricks/Unity Catalog, and developing LLM-based applications. A Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field is required.
What are the key responsibilities of the Senior Principal Machine Learning Engineer?
You will design production backend systems for agentic AI applications, own GitOps and engineering-governance standards, and make key MLOps/platform decisions. Additionally, you will own the architecture for agentic AI systems, lead the evaluation of new agent frameworks, and mentor other engineers on architecture and design patterns.
What is the application process and how can I request accommodations?
To apply, submit your resume. If you have a disability and require an accommodation during the application process, you can submit a request by completing the accommodation request form available at https://careers.lilly.com/us/en/workplace-accommodation.

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  • Verified H-1B salary data
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Job Information

Source: manual
AI Relevance: 72/100 (Relevant)
Remote Type: onsite
Allowed Locations: Worldwide
Skills & Tags:
eli lilly machine learning LLM
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