Senior MLOps Engineer

TheGuarantors

full-time

Posted on: 9/22/2025

Origin: • 🇺🇸 United States • New York

✨ AI Apply

💰 $200,000 - $220,000 per year

Senior

AirflowCloudDockerKubernetesPythonTerraform

About the role

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 production
Implement centralized feature stores, model registries, and experiment tracking tools
Build intelligent exception handling frameworks for automated model recovery, schema drift detection, and fallbacks
Architect cloud-native, modular infrastructure (Terraform, Kubernetes, SageMaker, MLflow) for model serving and deployment
Integrate solvers and simulation frameworks to support operations research use cases
Monitor model health with dashboards and alerts for data drift, bias, and latency across batch and real-time scoring
Enable rapid experimentation with reproducible workflows and automated ML CI/CD
Embed governance practices: audit logging, explainability tooling, and PII protection
Partner with domain squads to align AI deployments with business KPIs and mentor engineering/data teams

5+ years of experience in MLOps, ML Engineering, or DevOps, with a strong record of deploying machine learning models at scale
Ph.D. in Math, Engineering, Statistics, Economics preferred
Proficiency in Python and orchestration tools (Airflow, Prefect, Dagster)
Experience with model lifecycle tooling (MLflow, SageMaker, Vertex AI)
Hands-on experience with containerization (Docker) and orchestration (Kubernetes/EKS)
Experience with 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 (fail-soft design, data quality validation, anomaly flagging)
Experience 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
Bonus: Background in fintech, insurance, pricing analytics, or risk modeling