Senior Machine Learning Engineer
Core
Design, deploy, and maintain production-grade MLOps pipelines, model integration APIs, and scalable inference systems for life sciences clients.
Role type
Senior Machine Learning Engineer (Production MLOps & Systems Architecture)
Builds
Production ML systems, model integration APIs, scalable MLOps pipelines, and low-latency inference microservices for life sciences and manufacturing.
Domain
Life Sciences (computational biology, drug discovery) and Chemical/Process Engineering (API manufacturing, batch optimization).
Deliverable
production ML models
Required skills
Python, C++, PyTorch, TensorFlow, Scikit-learn, Triton Inference Server, TorchServe, MLflow, Kubeflow, Kubernetes, Docker, FastAPI, gRPC, AWS/Azure, CI/CD, feature stores, drift detection
Preferred skills
GxP environment experience, Chemical Engineering or Bio-process Engineering background, AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional
Technologies
Triton Inference Server, TorchServe, MLflow, Kubeflow, Databricks ML runtime, Kubernetes, Docker, FastAPI, gRPC, AWS, Azure
Responsibilities
Design and maintain production MLOps pipelines for continuous training, deployment, and monitoring; integrate ML models into OT and chemical process control systems; build low-latency microservices for model serving; establish enterprise MLOps standards and CI/CD best practices.
Seniority
Senior, hands-on IC