You'll be our first MLOps engineer in the US, working closely with the MLOps team in India so customer work reaches production in days, not weeks. Most of your work is behind the scenes, with customer conversations when the technical details matter.
What You'll Do
Build for the customer
Productionize customer-tailored predictive models, configurations, and scoring workflows with validation, release, monitoring, and rollback paths.
Build and improve UI workflows for training triggers, model refreshes, validation outcomes, model status, and operational troubleshooting.
Enhance model dashboards that make model health, score changes, data quality, refresh status, and production issues understandable to internal users and customers.
Build or modify the APIs, backend services, data contracts, and orchestration workflows behind those experiences.
Automate model retraining, validation, promotion, and deployment workflows for customer engagements.
Create safeguards for data quality, score distributions, threshold changes, and edge cases before model changes reach production.
Monitor models and services for drift, data shifts, reliability issues, and operational regressions.
Productize for everyone
Turn repeatable customer solutions into reusable product and platform capabilities with Product, Data Science, and Platform Engineering.
Build deployment templates, reusable workflow components, and configuration-driven interfaces that support many customers without one-off operational work.
Create self-service or assisted tools for teams to initiate training, monitor refreshes, review validation results, and troubleshoot model issues safely.
Improve the MLOps platform where needed, including Kubernetes workloads, compute, pipelines, CI/CD, deployment tooling, and observability.
Establish clear runbooks, release checklists, audit trails, and support processes for model operations.
Identify recurring customer pain points and convert them into product requirements, platform improvements, or durable operational tooling.
What You'll Bring
6+ years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or related production engineering roles.
Experience owning machine-learning systems in production, including training, deployment, monitoring, retraining, and incident response.
Strong Python and SQL skills, including APIs, backend services, asynchronous workflows, testing, CI/CD, and observability.
