Senior Machine Learning Engineer

Xenon7; Columbus (OH); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Xenon7 is seeking a Senior Machine Learning Engineer to design and run production ML systems for a life sciences client in a hybrid work setting.

You will build and run MLOps pipelines, implement drift detection, and integrate ML into OT and API manufacturing workflows while scaling to high-throughput inference across cloud and edge environments.

Qualifications

• 10–20+ years of senior-level software, MLOps, and infrastructure scaling experience.
• Proven ability delivering production ML systems in specialized non-standard domains.
• Unrestricted US work authorization; ability to work 3 days onsite in Indianapolis area.

Responsibilities

• Design, deploy, and maintain production-grade MLOps pipelines and infrastructure.
• Implement automated model drift detection, performance monitoring, and self-healing inference.
• Operationalize production ML models into OT, API manufacturing workflows, and chemical process control.
• Deploy models for batch processing, process control optimization, and real-time QA.
• Build low-latency, high-throughput model serving microservices with FastAPI, Triton, TorchServe.
• Containerize and orchestrate ML workloads across cloud/edge with Kubernetes, Docker, Kubeflow, MLflow.
• Collaborate with chemical engineers and scientists to produce production-ready ML solutions.
• Define enterprise MLOps standards, governance, and CI/CD practices.

Skills

Python

C++

PyTorch

TensorFlow

Scikit-learn

FastAPI

gRPC

AWS

Azure

CI/CD pipelines

Kubernetes

Docker

Kubeflow

MLflow

Databricks ML runtime

Triton Inference Server

TorchServe

FastAPI/gRPC

SCADA

MES

Tools

Kubernetes

Docker

Kubeflow

MLflow

Triton Inference Server

TorchServe

Databricks ML runtime

AWS

Azure

Job description

Senior Machine Learning Engineer to design and run production ML systems for a life sciences client in a hybrid environment.

Responsibilities

• Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous training, deployment, versioning, and monitoring.
• Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments.
• Operationalize production ML models and integrate them into operational technology (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 model serving microservices and architectures using FastAPI, Triton Inference Server, and TorchServe.
• Containerize and orchestrate ML workloads across distributed cloud and edge systems with Kubernetes, Docker, and pipeline engines such as Kubeflow and MLflow.
• Partner with chemical engineers, computational biologists, and software architects to turn operational friction into production-ready ML solutions.
• Define enterprise MLOps standards, model governance, and CI/CD best practices across the full ML lifecycle.

Requirements

• 10–20+ years of senior-level experience across software engineering, MLOps, production ML deployment, and infrastructure scaling.
• Proven ability to deliver production ML systems in highly specialized, non-standard domains, including transitions between process/chemical engineering ML and clinical or scientific research applications.
• Must hold unrestricted US work authorization (no sponsorship available).Ability to work 3 days per week onsite in the Indianapolis, IN area (regional/EST candidates may travel onsite).
• Pragmatic problem-solving and strong collaboration; able to explain complex MLOps architecture to cross-functional engineering teams.
• Advanced Python and C++ plus deep expertise with PyTorch, TensorFlow, or Scikit-learn.
• Demonstrated expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime.
• Hands-on experience with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and AWS/Azure ecosystems.
• Experience building real-time model monitoring, feature stores, drift detection, and integrations with enterprise data pipelines.
• Deep exposure applying ML to either scientific/clinical domains (drug discovery, small or large molecule, computational biology) or chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization).

Required Technologies

• Python, C++, PyTorch, TensorFlow, Scikit-learn
• Triton Inference Server, TorchServe, MLflow, Kubeflow, Databricks ML runtime
• Kubernetes, Docker, CI/CD pipelines
• FastAPI, gRPC, AWS, Azure
• Feature stores, SCADA, MES

Location & Contract Details

• Location: Columbus, OH (hybrid)
• Onsite requirement: 3 days per week onsite in the Indianapolis, IN area
• Job type: Contract (Full-Time / enterprise project engagement)
• Engagement route: Outsourced via Xenon7

Nice-to-Haves & Certifications

• Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or related STEM discipline.
• Experience operationalizing ML models in regulated GxP environments in life sciences or specialty chemicals.
• AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials.

Role Focus

• Not a Data Scientist or exploratory R&D role focused on Jupyter-style modeling or research algorithms.
• Not a non-coding architecture position; emphasis is hands-on MLOps and engineering execution, including model deployment and infrastructure creation.
• Not fully remote; hybrid onsite commitment is required.