Machine Learning & Data Operations Engineer

Position Summary

As a Machine Learning & Data Operations Engineer on Tune Lab, 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.

• 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.

• 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.

• 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.

• 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).