Senior Machine Learning Engineer (Ops)

• Migrate box and barcode detection pipelines to cloud infrastructure following MLOps best practices
• Build and maintain cloud-based training pipelines for GPU models, ensuring efficient resource utilization and reproducibility across experiments
• Build and maintain CI/CD pipelines for deployment across production and non-production environments
• Implement automated rollback, canary, and blue-green deployment strategies for ML microservices
• Build out a multi-tenant MLOps platform using tools like Prefect, ZenML, or similar orchestration frameworks
• Establish a centralized model registry and versioning system for all production assets
• Instrument observability across the ML stack — logging, metrics, and distributed tracing — to ensure reliability at scale

What You'll Need

• 6+ years of industry experience (outside academia) in ML engineering, MLOps, or infrastructure engineering
• Deep operational fluency with Kubernetes and Docker for ML workload orchestration
• Strong production-grade Python skills with a track record of hardening research code into scalable microservices
• Hands-on experience with CI/CD for ML (e.g., GitHub Actions, GitLab CI) and model serving frameworks (e.g., KServe, SageMaker, Vertex AI Endpoints)
• Experience with pipeline orchestration and model lifecycle tools such as Airflow, MLflow, Kubeflow, or Flyte
• Cloud: Deep experience with one major cloud provider (e.g., AWS, Azure, or GCP), including building training pipelines in the cloud for GPU models
• Proven ownership of production system reliability, including SRE principles, observability stacks, and automated failure safeguards

Nice to Have

• Prior experience building end-to-end MLOps pipelines (data, model, and inference) from scratch
• Domain experience in logistics, supply chain, or robotics-adjacent cloud platforms
• Familiarity with feature stores and training/serving data consistency patterns
• Experience with Infrastructure as Code tools such as Terraform