Manager, Machine Learning Operations – Delivery

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

📋 Description

• Architect and execute comprehensive machine learning operational solutions, encompassing cloud infrastructure, model deployment, performance enhancement, and security oversight of ML models in a production environment.

• Lead and cultivate a team comprising ML engineers, Data Engineers, Software Engineers, and DevOps experts.

• Collaborate with various cross-functional teams to assimilate ML and cloud technologies within products and services.

• Establish and uphold ML Ops best practices, which include continuous integration, continuous delivery, automated testing, security by design, and scalable infrastructure solutions.

• Propel the utilization of cloud technologies and services for ML model training and deployment, focusing on optimizing performance and managing costs effectively.

• Oversee the development and upkeep of data pipelines for data ingestion, processing, and model training.

• Spearhead the department's initiatives in enterprise Disaster Recovery, Business Continuity, and Record Retention efforts.

• Assess and incorporate innovations in ML Ops, DevOps, SecOps, and Cloud Infrastructure and Architecture.

• Guarantee compliance with technical, security, and regulatory standards, including infrastructure security, data privacy, and model governance.

⛳️ Requirements

• A Bachelor's degree (B.S. or higher) in Computer Science, Engineering, Mathematics, a related STEM field, or equivalent professional experience; an M.S. is preferred.

• Over 3 years of experience managing high-performing software development teams, with at least 5 direct reports.

• At least 2 years of close collaboration with, or 1 year of management experience in, Platform Engineering teams.

• More than 6 years of experience deploying solutions in Java, JavaScript, and Python.

• Over 3 years of experience in machine learning engineering, with a demonstrated history of deploying and overseeing ML models in production settings.

• Proficiency in ML Ops, DevOps methodologies, and API development.

• Minimum of 3 years managing data infrastructure and data pipelines.

• At least 3 years of experience in creating and supporting serverless multi-cloud deployments and provisioning using AWS and Azure.

• More than 3 years of experience in building and deploying Docker solutions.

• Over 3 years of experience designing and deploying advanced Terraform solutions.

• At least 3 years of knowledge in network technologies (VPCs).

• Previous experience in developing and maintaining Disaster Recovery Operational Recovery Documents and Business Continuity plans.

• A minimum of 1 year of experience using RunScope, JMeter, and Secrets Manager to oversee sensitive assets.

• At least 1 year of experience in QA Test Automation and CI/CD automation using Code Pipeline, Code Build, and Code Deploy.

• At least 1 year of experience with Git Hooks and managing pre-commit validation.

• Familiarity with designing and implementing Terraform infrastructure.

• Understanding of AWS and Azure budget management.

• Excellent communication skills, with the ability to convey complex technical concepts to varied audiences.

• Experience in implementing agentic operations at scale is highly desirable.

• Familiarity with vector databases, relational databases, NLP model deployment, and large dataset analysis is highly desirable.

• Experience with clustering, dimensionality reduction, and language model deployment is a plus.

• Familiarity with Record Retention policies and initiatives is a plus.

• Ability to successfully pass a background check.

🏝️ Benefits

• Comprehensive group health insurance benefits (medical, vision, dental).

• Flexible Spending Account (FSA) and Health Savings Account (HSA) healthcare options.

• Life and accidental insurance coverage.

• Assistance for adoption and fertility-related needs.

• Paid parental leave of up to 6 weeks.

• Short-term and long-term disability coverage.

• Paid time off for vacations, personal needs, and sick leave.

• Choice Time Off (CTO) of up to 17 days per calendar year for newly hired employees.

• Up to 11 paid holidays annually.

• 401(k) savings and investment plan or deferred compensation plan (if eligible).

• Employer contribution of 100% on the first 3% of contributions for eligible employees.

• Discretionary annual bonus opportunities.

• Reasonable accommodation support for applicants with disabilities.