Engineer – MLOps, Scientific Platforms

Eli Lilly and Company

. Build and maintain end-to-end ML deployment pipelines, including experiment tracking, model versioning, containerized model serving, and automated retraining triggers .

Posted 9/23/2026full-timeUnited StatesMid-LevelSenior💰 $66,000 - $165,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining end-to-end ML deployment pipelines, including model versioning, containerized model serving, and automated retraining. Proficient in deploying predictive methods in cheminformatics and bioinformatics while ensuring operational monitoring and API integration.

Highest-signal resume keywords

MLOpsPythonKubernetesAWSTensorFlow

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel MonitoringFeature EngineeringCI/CD AutomationContainerizationAPI DevelopmentExperiment TrackingDrift DetectionBioinformaticsComputational Biology

Tools & Technologies

PyTorchScikit-learnMLflowW&BKubeflowRESTful APIsGRPCDVCMCPLangChain

Industry Keywords

PharmaceuticalBiotech ResearchData FoundryHPC WorkloadsEvent-Driven Architectures

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformGRPCKubernetesPythonPyTorchScikit-LearnTensorflowC++

About the role

Key responsibilities & impact

Build and maintain end-to-end ML deployment pipelines, including experiment tracking, model versioning, containerized model serving, and automated retraining triggers
Develop model registry infrastructure and feature engineering pipelines
Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems
Build dashboards and metrics for pipeline execution, API latency, token usage, prediction quality, and system health
Establish structured logging and tracing infrastructure
Deploy predictive and analytical methods in cheminformatics, structural biology, bioinformatics, and reaction informatics
Build serving infrastructure for synchronous, asynchronous, batch, and agent-invoked workloads
Define API contracts, documentation standards, and testing frameworks
Build and operate cloud-native model-serving infrastructure using containers, Kubernetes, and infrastructure-as-code
Develop CI/CD pipelines with automated validation, A/B testing, canary deployments, and rollback procedures
Integrate model serving with Data Foundry data pipelines
Partner with Frontier AI and Tech@Lilly to expose scientific tools through REST APIs and MCP-compatible endpoints
Collaborate on API latency, throughput, and graceful-degradation requirements
Work with Methods4Insight scientists to implement uncertainty quantification and confidence metrics

Requirements

What you’ll need

B.S. or M.S. in Computer Science, Data Science, Machine Learning, Bioinformatics, Computational Biology, or related field
3+ years of experience in MLOps, ML engineering, or scientific platform development
Authorized to work in the United States on a full-time basis
Lilly will not provide support for or sponsor work authorization or visas
Pharmaceutical or biotech research industry experience preferred
Strong Python skills
Experience with PyTorch, TensorFlow, scikit-learn, MLflow, W&B, Kubeflow, or similar tools
Experience building and deploying production model-serving infrastructure, containerized endpoints, RESTful/gRPC APIs, and operational monitoring
Working knowledge of AWS, Azure, or GCP, Kubernetes, and CI/CD automation
Experience operationalizing scientific or computational models
Experience with model monitoring, drift detection, and automated retraining systems
Familiarity with API gateway patterns, event-driven architectures, and service mesh technologies
Experience with feature stores, DVC, or experiment tracking at scale
Exposure to AI agent frameworks such as MCP and LangChain
Experience with C, C++, CUDA, or GPU-accelerated computing
Familiarity with containerizing HPC workloads using Singularity/Apptainer

Benefits

Comp & perks

Company bonus depending in part on company and individual performance
Company-sponsored 401(k)
Pension
Vacation benefits
Medical, dental, vision, and prescription drug benefits
Flexible benefits, including healthcare and/or dependent day care flexible spending accounts
Life insurance and death benefits
Time off and leave of absence benefits
Well-being benefits, including employee assistance program and fitness benefits
Employee clubs and activities
Employee resource groups (ERGs)