Senior MLOps Engineer

TheGuarantors

147d•$200,000 - $220,000•Remote

About The Position

We are building a next-generation AI/ML operating model at TheGuarantors—anchored by a centralized AI Platform & MLOps team and empowered domain-focused squads across Pricing, Risk, Claims, GTM, and Sales. As a Senior MLOps Engineer, you will be a foundational member of the platform team, building scalable, governed infrastructure that accelerates the development and deployment of machine learning and operations research models. You’ll work closely with data scientists and engineers to ensure fast, safe, and reliable delivery of high-impact models—from pricing elasticity and dynamic underwriting to claims automation and lead scoring.

Requirements

5+ years of experience in MLOps, ML Engineering, or DevOps, with a strong record of deploying machine learning models at scale
Proficiency in Python and orchestration tools (Airflow, Prefect, Dagster), plus experience with model lifecycle tooling (MLflow, SageMaker, Vertex AI)
Hands-on experience with containerization (Docker), orchestration (Kubernetes/EKS), and infrastructure-as-code (Terraform, CloudFormation)
Deep understanding of the machine learning lifecycle, including feature engineering, testing, observability, and rollback strategies
Familiarity with exception handling patterns in production ML (e.g., fail-soft design, data quality validation, anomaly flagging)
Experience supporting or integrating optimization libraries, solvers, and simulation workflows for operations research
Knowledge of data privacy and compliance requirements for deploying models in regulated industries
Excellent communication skills and a collaborative mindset for working cross-functionally across technical and business teams

Nice To Haves

Ph.D. in Math, Engineering, Statistics, Economics preferred
Background in fintech, insurance, pricing analytics, or risk modeling

Responsibilities

Design and manage robust ML/AI pipelines to support scalable deployments across Pricing, Risk, Claims, GTM, and Sales
Collaborate with data scientists to operationalize supervised, unsupervised, and optimization models in real-world production systems
Implement reusable infrastructure such as centralized feature stores, model registries, and experiment tracking tools
Build intelligent exception handling frameworks for automated model recovery, schema drift detection, and fallbacks
Architect infrastructure that supports dynamic pricing engines, loss prediction models, claims triage algorithms, and real-time lead scoring
Support operations research use cases by integrating solvers and simulation frameworks into model pipelines
Monitor model health using live dashboards and alerts for data drift, bias, and latency across both batch and real-time scoring
Enable rapid experimentation through reproducible workflows and automated CI/CD tailored for ML
Embed governance practices such as audit logging, explainability tooling, and PII protection into the MLOps layer
Future-proof our AI/ML stack with modular, scalable, cloud-native components (e.g., Terraform, Kubernetes, SageMaker, MLflow)
Partner with domain squads to align AI deployments with KPIs such as conversion uplift, pricing precision, loss ratio, and claims turnaround
Contribute to the evolution of our AI Platform strategy and evaluate next-gen MLOps tools to improve developer velocity and system resilience
Act as a mentor and thought partner across engineering and data teams to uplift the organization's model delivery capabilities