Machine Learning Engineer

Xenon7 works with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. This role supports a top-tier life sciences client.

• 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.
• 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.
• Build low-latency, high-throughput microservices and serving architectures (FastAPI, Triton Inference Server, TorchServe).
• Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, Kubeflow, MLflow.
• Partner directly with chemical engineers, computational biologists, and software architects.
• Establish enterprise MLOps standards, model governance, and CI/CD best practices.

• Senior-level proficiency (10–20+ years) in software engineering, MLOps, production ML system deployment, and infrastructure scaling.
• Demonstrated ability to deploy and maintain production ML systems across non-standard, highly specialized domains.
• Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area.
• Pragmatic problem-solving mindset and strong collaborative drive.
• Advanced Python, C++, and deep proficiency with PyTorch, TensorFlow, or Scikit-learn.
• Proven expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime.
• Hands-on expertise with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and cloud platform ecosystems (AWS/Azure).
• Experience building real-time model monitoring, feature stores, drift detection systems, and integration with enterprise data pipelines.
• Deep exposure to applying ML models in scientific/clinical domains OR chemical/process engineering environments.

• Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline.
• Direct experience operationalizing ML models inside regulated GxP environments.
• Certifications: AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent.

• Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7).
• Hybrid / 3-Day Onsite in Indianapolis, IN Metro.
• Not a fully remote position.