ML Engineer

Group 1001 is a consumer‑centric, technology‑driven family of insurance companies on a mission to deliver outstanding value and operational performance by combining financial strength, deep expertise and a can‑do culture.

We’re building AI/ML‑powered products that will transform how Group 1001 approaches pricing optimization, claims automation and risk intelligence. To do this at scale we need robust ML infrastructure—not just great models. As a Staff ML Engineer you’ll focus on the MLOps and infrastructure layer that makes ML production‑ready: model serving, feature pipelines, experiment tracking and CI/CD for ML. You’ll help shape our ML platform architecture, working alongside Platform Engineering teams to ensure ML workloads run reliably on our modern stack:
Snowflake, Dagster, Coalesce, Palantir and AWS Sage Maker.

• Partner with Data & Platform Engineering to define how ML workloads integrate with our Snowflake‑Dagster‑Palantir ecosystem.
• Evaluate and recommend tooling for the ML stack—balancing build vs. buy decisions against our scale and compliance needs.
• Contribute to platform roadmap discussions, advocating for infrastructure investments that accelerate ML delivery.
• Establish CI/CD pipelines for ML: automated testing, model validation, staged deployments and rollback capabilities using Sage Maker Pipelines, Step Functions or similar orchestration.
• Implement model monitoring and observability: drift detection, performance degradation alerts and automated retraining triggers.
• Architect ML workloads on AWS:
  Sage Maker (Training Jobs, Processing, Endpoints), EC2/EKS for custom serving, S3 for artifact storage, IAM for secure access patterns.
• Optimize for cost and performance—right‑sizing instances, spot instance strategies, auto‑scaling endpoints and efficient GPU utilization.
• Integrate ML infrastructure with our Dagster orchestration layer for end‑to‑end pipeline visibility.
• Mentor senior ML engineers and technical leads, developing the next generation of ML engineering leadership.

• MLOps & Model Serving
  :
  Hands‑on experience with model serving frameworks (Sage Maker Endpoints, Seldon Core, BentoML, Ray Serve, or Tensor Flow Serving); building and operating inference infrastructure at scale.
• CI/CD for ML
  :
  Building ML pipelines with Sage Maker Pipelines, Kubeflow, Airflow or Dagster; automated model testing, validation gates and deployment automation.
• AWS & Cloud Infrastructure
  :
  Strong AWS experience—Sage Maker, EKS/ECS, Lambda, Step Functions, S3, IAM; infrastructure‑as‑code (Terraform, CDK, Cloud Formation).
• Monitoring & Observability
  :
  Model monitoring, drift detection, alerting; tools like Evidently, Why Labs, Sage Maker Model Monitor or custom solutions.
• Core ML Fundamentals
  :
  Working knowledge of Python, ML frameworks (PyTorch, Tensor Flow, scikit‑learn) and model evaluation—enough to partner effectively with data scientists.
• Feature Engineering Infrastructure
  :
  Experience with feature stores (Sage Maker Feature Store, Feast, Tecton or similar); designing feature pipelines for both batch and real‑time serving.
• Experiment Tracking & Registry
  : MLflow, Weights & Biases, Sage Maker Experiments or similar; establishing reproducibility and governance across ML projects.

• Palantir Foundry, Kubernetes, Bedrock, cost optimization strategies for ML workloads.

• Bachelor’s degree in Computer Science, Data Science, Engineering or related field.
• Master’s degree or equivalent experience preferred.

• 6–10 years in ML engineering, MLOps or platform engineering with a focus on product ionizing ML systems.
• Demonstrated experience building ML infrastructure that others build upon—serving layers, feature stores or MLOps tooling.
• Track record of improving ML delivery velocity through infrastructure and automation.
• Proven ability to work cross‑functionally with data scientists, platform engineers and stakeholders.
• Experience mentoring and developing senior engineers and technical leaders.
• Strong executive presence with ability to influence stakeholders at all levels of the organization.

• Experience in…