Senior Machine Learning Engineer

Xenon Seven

. Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring .

Posted 9/22/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in designing, deploying, and maintaining production-grade MLOps pipelines and infrastructure, with a strong focus on integrating ML models into operational technology and ensuring compliance in regulated environments. Proficient in advanced programming and cloud technologies, with a proven ability to collaborate across multidisciplinary teams to deliver scalable ML solutions.

Highest-signal resume keywords

MLOps Pipeline DesignProduction ML System DeploymentAdvanced Python and C++Kubernetes and Docker ExpertiseAWS Certified Machine Learning – Specialty

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

MLOpsProduction ML SystemsModel Drift DetectionPerformance MonitoringFastAPITriton Inference ServerTorchServeKubernetesDockerCI/CD Pipelines

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

MLflowKubeflowAWSAzureDatabricks ML Runtime

Certifications & Qualifications

AWS Certified Machine Learning – SpecialtyDatabricks Certified Machine Learning Professional

Industry Keywords

Chemical EngineeringBio-process EngineeringClinical ResearchGxP EnvironmentsProcess Control Optimization

Tech Stack

Tools & technologies

AWSAzureCloudDockerGRPCKubernetesMicroservicesPythonPyTorchScikit-LearnTensorflowC++

About the role

Key responsibilities & impact

Design, deploy, and maintain 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, 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 using FastAPI, Triton Inference Server, and TorchServe
Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, Kubeflow, and MLflow
Partner with chemical engineers, computational biologists, and software architects to translate operational needs into production ML solutions
Establish enterprise MLOps standards, model governance, and CI/CD best practices
Own architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines
Collaborate onsite with process engineers, life science researchers, and platform engineering teams

Requirements

What you’ll need

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 specialized domains, including process/chemical engineering ML and clinical/scientific research applications
Must hold unrestricted US Work Authorization; no sponsorship available
Able to work 3 days per week onsite in the Indianapolis, IN area
Advanced Python and C++
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 AWS/Azure cloud ecosystems
Experience building real-time model monitoring, feature stores, drift detection systems, and integrating 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 related STEM discipline (nice-to-have)
Direct experience operationalizing ML models inside regulated GxP environments (nice-to-have)
AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials (nice-to-have)