Senior MLOps Engineer - Production ML for Life Sciences

Xenon Seven; Bloomington (IN); Hybrid; USD 130,000 - 180,000; Full time

Job summary

Xenon7 is seeking a Senior Machine Learning Engineer to lead production ML initiatives for a life sciences client. You will own architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines across OT, API manufacturing workflows, and process control systems.

You will collaborate with chemical engineers, computational biologists, and software architects to deliver low-latency, enterprise-grade ML solutions that scale.

Qualifications

• Senior-level proficiency (10-20+ years) in software engineering, MLOps, production ML system deployment, and infrastructure scaling.

Responsibilities

• Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring.
• Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments.
• Operationalize and integrate production ML models into OT, API manufacturing workflows, and chemical process control systems.
• Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases.
• Build low-latency, high-throughput microservices and serving architectures for model deployment into live production applications.

Skills

Python

C++

PyTorch

TensorFlow

Scikit-learn

Tools

Triton Inference Server

TorchServe

MLflow

Kubeflow

Databricks ML runtime

Kubernetes

Docker

FastAPI

gRPC

AWS

Azure

Job description

Xenon7 is seeking a Senior Machine Learning Engineer to lead production ML initiatives for a life sciences client. You will own architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines across OT, API manufacturing workflows, and process control systems.

You will collaborate with chemical engineers, computational biologists, and software architects to deliver low-latency, enterprise-grade ML solutions that scale.