Senior Machine Learning Engineer

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.