Senior Machine Learning Engineer

Senior Machine Learning Engineer to lead production ML systems architecture and hands-on delivery for a life sciences client.

Responsibilities

• Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous training, deployment, model versioning, and monitoring.
• Implement automated model drift detection, performance monitoring, and self-healing inference pipelines for high-reliability environments.
• Operationalize and integrate production ML models into operational technology (OT), API-driven 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 microservices and serving architectures for model deployment into live production applications.
• Containerize and orchestrate ML workloads across distributed cloud and edge environments using Kubernetes, Docker, and pipeline engines like Kubeflow and MLflow.
• Collaborate directly with chemical engineers, computational biologists, and software architects to convert operational friction into production-ready ML solutions.
• Define enterprise MLOps standards, model governance practices, 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 deploy and maintain production ML systems in specialized, non-standard domains, including transitions between process/chemical engineering ML and clinical/scientific research applications.
• Unrestricted US Work Authorization (no sponsorship available) and ability to work 3 days per week onsite in the Indianapolis, IN area.
• Pragmatic problem-solving approach with strong collaboration and ability to communicate complex MLOps architecture to cross-functional engineering teams.
• Advanced Python and C++, plus deep proficiency with PyTorch, TensorFlow, or Scikit-learn.
• Experience with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime.
• Hands-on expertise with Kubernetes, Docker, CI/CD pipelines, and FastAPI/gRPC, plus cloud ecosystems such as AWS or Azure.
• Experience building real-time model monitoring, feature stores, drift detection systems, and integration with enterprise data pipelines.
• Deep exposure 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).

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

• Indianapolis, IN Metro (Hybrid / 3-Day Onsite)
• Open to regional/EST candidates with onsite travel

Contract Type

• Contractor Full-Time / Enterprise project engagement (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 within Life Sciences or Specialty Chemicals.
• AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials.

What This Role Is Not

• Not a Data Scientist or exploratory R&D specialist: focus is production MLOps pipelines, inference engines, and model integration code (not exploratory analysis or standalone Jupyter notebook modeling).
• Not a non-coding architect: requires hands-on MLOps and software engineering execution, including direct model deployment and infrastructure creation.
• Not fully remote: requires hybrid commitment of 3 onsite days per week at the client site in Indianapolis.