Senior MLOps Engineer, Forward Deployed

Forward Deployed Engineering · Predictive track · Senior

TEAM
Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering

REPORTS TO
MLOps engineering lead

LEVEL
Senior (6+ years) 

LOCATION
United States

THE ROLE IN ONE PARAGRAPH

This is a hands-on MLOps engineering role. You will work across model training and refresh workflows, APIs, orchestration, data pipelines, production monitoring, and user-facing experiences such as model dashboards and training controls.

When a customer needs a better model, a refresh workflow, or visibility into model health, you will help make it real: build the experience, connect it to reliable backend systems, validate the outcome, and operate it safely in production.

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.
• Experience building or materially contributing to internal tools, operational dashboards, or customer-facing product workflows.
• Comfortable debugging across the stack: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure.
• Experience releasing changes safely across multiple customers or production environments, including staged rollout, impact checks, and rollback.
• Working knowledge of Kubernetes and cloud infrastructure.
• Strong communication and judgment when balancing customer urgency, usability, reliability, and long-term maintainability.
• Nice to have: Fast API and Frontend experience.
• Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration.
• AWS, S3, Hive, distributed compute, and multi-tenant SaaS experience.
• Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience.
• B2B SaaS, go-to-market data, or predictive modeling experience.

What Success Looks Like

Within 3 months

• Understand the predictive-model lifecycle and improve at least one meaningful deployment, support, or visibility bottleneck.
• Bring clear release, monitoring, and support plans to active customer model engagements.

Within 6 months

• Common training, refresh, validation, and deployment workflows are materially more automated.
• Model dashboards or internal workflows provide clear visibility into model status, health, and refresh progress.
• Customer model changes move to production faster, with stronger operational controls.

Within 12 months

• At least one customer-specific solution has become a reusable product or platform capability.
• The team can reliably deliver and operate customer-tailored models without fragile manual processes or overnight handoffs.

Why this role is different: You won’t only maintain ML infrastructure. You’ll build the product experiences and production systems that make predictive models easier to launch, understand, refresh, and trust—then turn what works for one customer into something that benefits many.