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# Senior Machine Learning Engineer

- **Company:** Xenon Corporation
- **Location:** Columbus, OH, United States (Remote available)
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Batch Processing, C++ (Programming Language), Computational Biology, Continuous Integration, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Systems Architecture, Systems Integration, Management of Software Versions, Cloud Platform System, Pytorch, Fastapi, Containerization, Scikit Learn, Kubernetes, Information Technology, Low Latency, Performance Monitor, Machine Learning Operations, Grpc, GXP, Docker, Databricks, Programming Languages, Microservices
- **Published:** September 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fb519a1be92b285c

## About the Role

Experience & Mindset

 * Experience: Senior-level proficiency (10-20+ years) in software engineering, MLOps, production ML system deployment, and infrastructure scaling.
 * Domain Adaptability: Demonstrated ability to deploy and maintain production ML systems across non-standard, highly specialized domains (e.g., transition between process/chemical engineering ML and clinical/scientific research applications).
 * Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area.
 * Culture & Communication: Pragmatic problem-solving mindset, strong collaborative drive, and the ability to articulate complex MLOps architecture to cross-functional engineering teams.

Must-Have Technical Stack

 * Languages & Frameworks: Advanced Python, C++, and deep proficiency with PyTorch, TensorFlow, or Scikit-learn.
 * MLOps & Serving: Proven expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime.
 * Infrastructure & Orchestration: Hands-on expertise with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and cloud platform ecosystems (AWS/Azure).
 * Monitoring & Integration: Experience building real-time model monitoring, feature stores, drift detection systems, and integration with enterprise data pipelines.

Domain Competency (Scientific & Process Focus)

 * Deep exposure to applying ML models in either scientific/clinical domains (drug discovery, small/large molecule, computational biology) OR chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization).

Nice-to-Haves & Certifications

 * Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline.
 * Direct experience operationalizing ML models inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors.
 * Certifications: AWS Certified Machine Learning - Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials.

## Description

We are seeking a Senior Machine Learning Engineer with extensive experience in production MLOps, model deployment, and system scaling to drive ML engineering initiatives for a top-tier life sciences client. This role sits at the critical intersection of production ML infrastructure, life science research, and manufacturing process engineering.

In this position, you will own the architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines. You will bridge complex domains-from computational biology, small and large molecule research, and clinical trial analytics to active pharmaceutical ingredient (API) manufacturing processes, batch optimization, and industrial automation ML. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, life science researchers, and platform engineering teams to build robust, low-latency ML systems that scale across the enterprise., Production MLOps & Systems Architecture

 * Design, deploy, and maintain robust, 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.

Process Engineering & Manufacturing ML Integration

 * Operationalize and integrate production ML models 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.

Scalable Inference & System Integration

 * Build low-latency, high-throughput microservices and serving architectures (FastAPI, Triton Inference Server, TorchServe) for model deployment into live production applications.
 * Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, and modern pipeline engines (Kubeflow, MLflow).

Technical Leadership & Domain Alignment

 * Partner directly with chemical engineers, computational biologists, and software architects to translate operational friction into production-ready ML engineering solutions.
 * Establish enterprise MLOps standards, model governance, and CI/CD best practices across the full machine learning operational lifecycle.