ML Ops Engineer

Job Title: Associate Machine Learning Operations Engineer Location: 100% Remote (Candidates outside PST strongly preferred) Hourly Pay Rate: $60–70/hr W2 (Contract-to-Hire) Conversion Salary: ~$130,000 (midpoint) Contract Duration: 6 months (with conversion as early as 3 months possible) Schedule: Full-time (40 hours per week, standard business hours) Introduction We are seeking an Associate Machine Learning Operations (MLOps) Engineer to help design, implement, and maintain machine learning operations pipelines. This is a contract-to-hire opportunity ideal for someone with a data engineering background and an engineering mindset who enjoys scaling machine learning models into production environments. Unlike data science R&D roles, this position is focused on operationalizing and scaling POCs from the Data Science team into production systems. Required Skills & Qualifications • Minimum 3 years of relevant professional experience • Bachelor’s degree in Computer Science, Data Science, or related field (or equivalent combination of education and experience) • Hands-on experience in data engineering with focus on machine learning operations (MLOps) • Proficiency in Python and knowledge of data pipeline architecture and transformations (Databricks preferred) • Familiarity with cloud platforms (Azure strongly preferred; AWS or GCP acceptable) • Experience with ETL pipeline design and maintenance • Understanding of SQL and database fundamentals • Knowledge of CI/CD concepts and tools (e.g., Jenkins, Git, Perforce) • Experience in machine learning model deployment and management • Familiarity with performance monitoring and optimization techniques • Strong understanding of DevOps and ML lifecycle principles Preferred Skills & Qualifications • Prior experience with scaling POCs into production environments • Strong problem-solving skills with the ability to troubleshoot model performance and pipeline issues • Experience ensuring compliance with data security and privacy regulations • Exposure to monitoring and logging frameworks for ML models in production • Ability to collaborate across data science, engineering, and operations teams Day-to-Day Responsibilities • Assist in designing and developing MLOps infrastructure for deploying and managing ML models • Collaborate with data scientists to integrate POCs into scalable production pipelines • Build and maintain automated workflows and pipelines to support efficient model deployment • Monitor performance of ML models and troubleshoot issues in pipelines or infrastructure • Ensure security, scalability, and compliance of MLOps infrastructure • Support documentation of MLOps processes, workflows, and system architecture • Stay current on emerging technologies and best practices in MLOps Company Benefits & Culture • Competitive W2 hourly pay with strong conversion potential • Contract-to-hire pathway with ~$130K target salary on conversion • 100% remote work with cross-functional collaboration across diverse teams • Opportunity to play a critical role in scaling ML models into enterprise production environments • Collaborative, growth-focused culture that values innovation and engineering excellence #TECH #Remote