Senior ML Ops Engineer – Forward-Deployed

This is a fully remote position, open to applicants in California.

📋 Description

• Implement customer-specific predictive models, configurations, and scoring workflows with validation, deployment, monitoring, and rollback mechanisms.

• Develop and enhance user interface workflows for training triggers, model refreshes, validation results, model status, and operational troubleshooting.

• Improve model dashboards that monitor model health, score variations, data quality, refresh status, and production challenges.

• Construct or modify APIs, backend services, data agreements, and orchestration workflows.

• Automate workflows for model retraining, validation, promotion, and deployment tailored for customer engagements.

• Establish safeguards for data integrity, score distributions, threshold adjustments, and edge cases.

• Oversee models and services for drift, data shifts, reliability concerns, and operational regressions.

• Transform repeatable customer solutions into reusable product and platform functionalities.

• Create deployment templates, reusable workflow components, and configuration-driven interfaces.

• Develop self-service or assisted tools for training, refresh monitoring, validation assessments, and troubleshooting.

• Enhance the MLOps platform, encompassing Kubernetes workloads, compute resources, pipelines, CI/CD, deployment tools, and observability.

• Set up runbooks, release checklists, audit trails, and support processes for model operations.

• Recognize recurring customer challenges and translate them into product requirements, platform enhancements, or operational tools.

• Collaborate closely with the MLOps team in India to ensure customer projects reach production in days rather than weeks.

• Report directly to the MLOps engineering lead.

⛳️ Requirements

• Over 6 years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or similar production engineering roles.

• Proven experience managing machine-learning systems in production, including training, deployment, monitoring, retraining, and incident management.

• Proficient in Python and SQL.

• Familiarity with APIs, backend services, asynchronous workflows, testing, CI/CD, and observability.

• Experience in building or significantly contributing to internal tools, operational dashboards, or customer-facing product workflows.

• Comfortable debugging across various layers: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure.

• Experience in safely releasing changes across multiple customers or production environments, including staged rollouts, impact assessments, and rollback strategies.

• Working knowledge of Kubernetes and cloud infrastructure.

• Strong communication skills and sound judgment in balancing customer urgency, usability, reliability, and long-term maintainability.

• Preferred: Experience with Fast API and Frontend development.

• Familiarity with Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration.

• Experience with AWS, S3, Hive, distributed computing, and multi-tenant SaaS solutions.

• Experience in forward-deployed roles, consulting, solutions, implementation, or customer-facing engineering.

• Experience in B2B SaaS, go-to-market data, or predictive modeling.

🏝️ Benefits

• Health coverage.

• Paid parental leave.

• Generous paid time off and holidays.

• Quarterly self-care days off.

• Stock options.

• Equipment and support for remote work and team collaboration.

• Learning and development initiatives.

• Access to LinkedIn Learning.

• Quarterly wellness education sessions.

• Wellness days.

• Events hosted by Employee Resource Groups (ERGs).

• 401K employer matching program.

• Life insurance.

• Disability insurance.

• Possibility of a bonus program or commission plan.

• Paid holidays.