Machine Learning & Data Operations Engineer
Eli Lilly and Company
September 02, 2026
Remote friendly (Indiana, United States)
United States
IT
Position Summary
As a Machine Learning & Data Operations Engineer on TuneLab, build ML/AI tools to accelerate Lilly’s drug discovery by deploying models, scaling inference, and creating trusted data pipelines with validation, monitoring, and governance.
Core Responsibilities
- Deploy models from research to production: packaging, versioning, promotion across dev/stage/prod on AWS/Azure/GCP and on-prem/hybrid.
- Build and run scalable inference services/APIs (batch, real-time, streaming) with low latency and high throughput.
- Operate model-serving infrastructure with containers/Kubernetes (autoscaling, canary/blue-green rollouts, rollback).
- Integrate models into researcher-facing tools and enterprise systems.
- Design and maintain secure data pipelines (batch, CDC, streaming) including embedding/vector/feature pipelines.
- Implement storage/retrieval for structured and unstructured scientific data.
- Build automated data-readiness/quality monitoring (anomaly/outlier detection; missing/invalid/structural checks; schema-drift detection with reporting).
- Author/validate model cards; run/automate model validation and evaluation; gate promotion on results.
- Implement production monitoring (latency/throughput, drift, quality) with alerting and remediation.
- Build robust software/platform solutions (microservices; REST/GraphQL), CI/CD, and IaC.
- Collaborate cross-functionally and with external partners; provide documentation/runbooks for internal users.
Required Qualifications
- Ph.D. in Computer Science or related computational field.
- Hands-on software engineering/architecture; systems/object-oriented + scripting (Go/Rust/Java/C++; Python/JS).
- Experience deploying to containers/serverless/Kubernetes and serving ML models in production.
- Data pipeline experience with relational/non-relational stores (e.g., PostgreSQL/MySQL/MongoDB).
- HTTP/REST API proficiency; CI/CD with test-driven development; distributed systems experience.
Preferred Qualifications
- MLOps/model-serving tooling (MLflow, Kubeflow, registries); model governance (model cards/eval).
- Data-quality/anomaly/schema-drift monitoring; streaming/CDC (Kafka, Spark).
- LLM patterns (RAG, tool-calling, orchestration) and inference optimization.
- IaC (Terraform), service mesh, observability; life-sciences exposure; federated/collaborative ML.
Benefits (as stated)
- Eligible for company bonus; 401(k) and pension; vacation.
- Medical/dental/vision/prescription coverage; flexible benefits (FSA); life insurance/death benefits; time-off/leave; well-being (EAP, fitness, clubs).
Application Instructions
- If you need accommodation to submit a resume, complete the workplace accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation.
As a Machine Learning & Data Operations Engineer on TuneLab, build ML/AI tools to accelerate Lilly’s drug discovery by deploying models, scaling inference, and creating trusted data pipelines with validation, monitoring, and governance.
Core Responsibilities
- Deploy models from research to production: packaging, versioning, promotion across dev/stage/prod on AWS/Azure/GCP and on-prem/hybrid.
- Build and run scalable inference services/APIs (batch, real-time, streaming) with low latency and high throughput.
- Operate model-serving infrastructure with containers/Kubernetes (autoscaling, canary/blue-green rollouts, rollback).
- Integrate models into researcher-facing tools and enterprise systems.
- Design and maintain secure data pipelines (batch, CDC, streaming) including embedding/vector/feature pipelines.
- Implement storage/retrieval for structured and unstructured scientific data.
- Build automated data-readiness/quality monitoring (anomaly/outlier detection; missing/invalid/structural checks; schema-drift detection with reporting).
- Author/validate model cards; run/automate model validation and evaluation; gate promotion on results.
- Implement production monitoring (latency/throughput, drift, quality) with alerting and remediation.
- Build robust software/platform solutions (microservices; REST/GraphQL), CI/CD, and IaC.
- Collaborate cross-functionally and with external partners; provide documentation/runbooks for internal users.
Required Qualifications
- Ph.D. in Computer Science or related computational field.
- Hands-on software engineering/architecture; systems/object-oriented + scripting (Go/Rust/Java/C++; Python/JS).
- Experience deploying to containers/serverless/Kubernetes and serving ML models in production.
- Data pipeline experience with relational/non-relational stores (e.g., PostgreSQL/MySQL/MongoDB).
- HTTP/REST API proficiency; CI/CD with test-driven development; distributed systems experience.
Preferred Qualifications
- MLOps/model-serving tooling (MLflow, Kubeflow, registries); model governance (model cards/eval).
- Data-quality/anomaly/schema-drift monitoring; streaming/CDC (Kafka, Spark).
- LLM patterns (RAG, tool-calling, orchestration) and inference optimization.
- IaC (Terraform), service mesh, observability; life-sciences exposure; federated/collaborative ML.
Benefits (as stated)
- Eligible for company bonus; 401(k) and pension; vacation.
- Medical/dental/vision/prescription coverage; flexible benefits (FSA); life insurance/death benefits; time-off/leave; well-being (EAP, fitness, clubs).
Application Instructions
- If you need accommodation to submit a resume, complete the workplace accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation.