Machine Learning Engineer
Posted 21 Sep 2026
Advertised as “Senior Machine Learning Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 10+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Contract
- Visa sponsorship
- Not offered
No salary listed for this role.
CertificationsAWS Certified Machine Learning - Specialty, Databricks Certified Machine Learning Professional
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)
About Xenon7
Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.
Job Summary
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.
Key Responsibilities
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.