Sr. ML Ops Engineer, Forward-Deployed

6sense•San Francisco, CA

8h•$191,475 - $260,830

About The Position

The Senior MLOps Engineer is a hands-on role on 6sense’s Forward Deployed Engineering Predictive track within Data Science & AI Engineering. The engineer works across model training and refresh workflows, APIs, orchestration, data pipelines, production monitoring, and user-facing experiences such as model dashboards and training controls. The role turns customer needs for better models, refreshed workflows, and model-health visibility into reliable backend systems and operational user experiences, validates outcomes, and operates changes safely in production. As the first MLOps engineer in the United States, this person works closely with the MLOps team in India to help deliver customer work to production quickly. Most work is behind the scenes, with customer conversations when technical details require them. The role also turns repeatable customer solutions into reusable product and platform capabilities, working with Product, Data Science, and Platform Engineering.

Requirements

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.
Ability to debug across the 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 Haves

FastAPI and frontend experience.
Experience with Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration.
Experience with AWS, S3, Hive, distributed compute, and multi-tenant SaaS.
Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience.
B2B SaaS, go-to-market data, or predictive modeling experience.

Responsibilities

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 user 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.
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 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.
Improve at least one meaningful deployment, support, or visibility bottleneck within the first three months.
Bring clear release, monitoring, and support plans to active customer model engagements.
Within six months, materially automate common training, refresh, validation, and deployment workflows and improve visibility into model status, health, and refresh progress.
Help customer model changes reach production faster with stronger operational controls.
Within twelve months, turn at least one customer-specific solution into a reusable product or platform capability and help the team reliably deliver and operate customer-tailored models without fragile manual processes or overnight handoffs.