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 – [email protected]
