ML Ops Engineer

What you'll do

• Design, build, and improve machine learning solutions in a dynamic cloud environment
• Explore data, develop models, run rigorous experiments
• Bring best approaches into production with reliable, reproducible workflow
• Work at the intersection of data science and engineering
• Package models, build inference paths, monitor performance, and iterate after launch
• Collaborate with engineers, product partners, and data scientists
• Communicate methods, results, and limitations to diverse audiences

Requirements

• Strong foundation in machine learning, statistics and experiment design
• Experience building models for real business or product problems
• Comfortable working with structured and unstructured data
• Able to compare approaches with clear metrics, error analysis, and sound judgment about tradeoffs
• Interest in modern ML including classical ML, deep learning, and LLM / GenAI workflows
• Proficient in Python and able to write clean, modular, testable code
• Experience developing and deploying ML solutions in a cloud environment, especially AWS
• Comfortable moving from prototype to production including packaging models, building inference paths, monitoring performance, and iterating after launch
• Independent engineer able to own work from problem framing to rollout
• Excellent written and spoken English
• Enjoy working closely with engineers, product partners, and other data scientists
• Clear communicator able to explain methods, results, and limitations to technical and non-technical audiences
• Master’s degree in Science or Engineering or equivalent practical experience

Tech stack

Benefits