Senior MLOps Engineer — Life Sciences (Hybrid, Indianapolis)

Xenon7; Indianapolis (IN); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Xenon7 is seeking a Senior Machine Learning Engineer to own the architecture and hands-on development of production ML systems for a life sciences client. You will design scalable MLOps pipelines, model integration APIs, and real-time inference services across cloud and edge environments.

The role requires 10–20+ years in software engineering and MLOps, with strong domain knowledge in computational biology or chemical/process engineering. Hybrid onsite (3 days/week) in Indianapolis is required.

Qualifications

• Senior-level proficiency in software engineering, MLOps, and production ML deployment.
• Experience applying ML in life sciences, API manufacturing, or process control domains.
• Must have unrestricted US work authorization and be able to work 3 days onsite weekly in Indianapolis.

Responsibilities

• Design, deploy, and maintain production-grade MLOps pipelines and infrastructure.
• Operationalize ML models into OT and manufacturing workflows.
• Build low-latency inference services and scalable deployment architectures.
• Collaborate with chemical engineers and biologists to align ML with process goals.
• Establish CI/CD and model governance across ML lifecycle.

Skills

Advanced Python

C++

PyTorch

TensorFlow

Scikit-learn

MLOps

System design

Communication

Education

Chemical Engineering / Bio-process Engineering / Computer Science background

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 own the architecture and hands-on development of production ML systems for a life sciences client. You will design scalable MLOps pipelines, model integration APIs, and real-time inference services across cloud and edge environments.

The role requires 10–20+ years in software engineering and MLOps, with strong domain knowledge in computational biology or chemical/process engineering. Hybrid onsite (3 days/week) in Indianapolis is required.