Sr Machine Learning Engineer
Core
Design core services, infrastructure, and governance controls for end-to-end machine-learning and generative-AI platforms to enable hundreds of practitioners to prototype, deploy, and monitor models securely and cost-effectively.
Role type
Senior individual-contributor machine learning engineer (platform & MLOps)
Builds
Enterprise-grade AI developer platforms, production-grade micro-services, and full-stack AI applications
Domain
Biotechnology / Enterprise AI & MLOps
Deliverable
production ML models
Required skills
Machine learning algorithms (regression, ensembles, deep learning, LLMs/RAG), Python, Java, Docker/Kubernetes, Cloud platforms (AWS/Azure/GCP), MLOps tools (Kubeflow, SageMaker Pipelines), Vector databases, Prompt engineering, Business case modeling (TCO/NPV)
Preferred skills
Biotechnology/pharma industry experience, Published thought leadership on GenAI, Master's degree in CS/Data Science, Agile/SAFe methodologies
Technologies
Kubeflow, SageMaker Pipelines, Open AI SDK, LangChain, Semantic Kernel, Docker, Kubernetes, AWS, Azure, GCP, GitHub Actions, Bedrock
Responsibilities
Engineer end-to-end ML pipelines (ingestion, training, evaluation, promotion); Harden research code into production micro-services with secure APIs; Build full-stack AI applications integrating models with UI/workflow engines; Optimize performance and cost at scale using algorithm selection and resource tuning; Instrument observability (metrics, tracing, drift detection); Embed security and responsible-AI controls; Contribute reusable platform components (feature stores, registries); Partner with data scientists on algorithm prototyping and benchmarking
Seniority
Senior, hands-on IC
