Principal ML Engineer

Title:

Principal ML Engineer Location:

Fully Remote (must be based in US)

Type:

FTE, Direct Hire Base Salary Range:

$170-210k •No third parties, please note sponsorship is not provided for this position•Our client is in the middle of a major push to embed AI and machine learning across their core business, from pricing intelligence and risk modeling to claims automation. They've built strong data foundations and now need a Staff/Principal Engineer to make ML production-ready at scale. This is not a research role. This is the person who makes sure great models actually ship, run reliably, and improve over time.

Key Responsibilities:

Engineer and operate the ML infrastructure layer, model serving, feature pipelines, experiment tracking, and deployment automation Define how ML workloads integrate with our data orchestration and warehousing ecosystem, balancing build-vs-buy decisions against scale and compliance requirements Establish CI/CD pipelines purpose-built for

ML:

automated testing, validation gates, staged rollouts, and rollback capabilities Implement model monitoring and observability frameworks, drift detection, performance alerting, and automated retraining triggers Optimize cloud ML infrastructure for cost and performance: right-sizing, spot instance strategies, auto-scaling, and efficient GPU utilization Partner with Platform Engineering to shape the long-term ML platform roadmap and advocate for infrastructure investments that accelerate delivery Mentor senior ML engineers and technical leads, building the next generation of ML engineering capability within the organization

Skilled Needed:

8+ years in ML engineering, MLOps, or platform engineering with a focus on productionizing ML systems Hands-on experience with model serving frameworks (SageMaker Endpoints, Ray Serve, BentoML, Seldon Core, or similar) Strong AWS experience: SageMaker, EKS/ECS, Lambda, Step Functions, S3, IAM, and infrastructure-as-code (Terraform, CDK, or CloudFormation) Experience building ML pipelines with orchestration tools such as Airflow, Kubeflow, Dagster, or SageMaker Pipelines Familiarity with model monitoring tooling: Evidently, WhyLabs, SageMaker Model Monitor, or custom-built solutions Experience with feature stores (Feast, Tecton, SageMaker Feature Store, or equivalent) for both batch and real-time serving Working knowledge of Python and core ML frameworks (PyTorch, TensorFlow, scikit-learn) Nice to have: Experience with Palantir Foundry, Kubernetes, AWS Bedrock Bachelor's degree in Computer Science, Data Science, Engineering, or a related field To be considered for the role please apply online or email an updated Resume to William Barclay at Oliver James - william.barclay@oliverjames.com Apply Now