Sr Machine Learning Engineer
Core
Design core services, infrastructure, and governance controls for building and scaling end-to-end machine-learning and generative-AI platforms, enabling hundreds of practitioners to prototype, deploy, and monitor models securely and cost-effectively.
Role type
Senior individual-contributor machine learning platform engineer (GenAI/MLOps)
Builds
Enterprise-grade AI developer platforms, production-grade micro-services, and full-stack AI applications
Domain
Life sciences / Enterprise AI infrastructure
Deliverable
production ML models | infrastructure
Required skills
Machine learning algorithms (regression, tree-based ensembles, deep learning, LLMs/RAG), Python, Java, Docker/Kubernetes, Cloud platforms (AWS/Azure/GCP), MLOps tools (Kubeflow, SageMaker Pipelines), Vector databases, Prompt engineering, Cost optimization, Observability, Responsible AI controls
Preferred skills
Experience with GenAI tooling (LangChain, Semantic Kernel), Business case modeling (TCO vs NPV), Stakeholder management
Technologies
Kubeflow, SageMaker Pipelines, Open AI SDK, Docker, Kubernetes, AWS, Azure, GCP, GitHub Actions, Bedrock, LangChain, Semantic Kernel
Responsibilities
Engineer end-to-end ML pipelines from data ingestion to automated promotion; Harden research code into production micro-services with secure APIs; Build full-stack AI applications integrating models with UI and workflow engines; Optimize performance and cost at scale using algorithm selection and resource tuning; Instrument comprehensive observability including drift and bias detection; Embed security and responsible-AI controls in model deployment; Contribute reusable platform components like feature stores and model registries; Partner with data scientists to prototype algorithms and benchmark production-readiness
Seniority
Senior, hands-on IC
