Senior Machine Learning Engineer

Role: Senior Machine Learning Engineer

Location: Remote

Type: Fulltime

Role Summary

• The Machine Learning Engineer is responsible for implementing, deploying, and maintaining machine learning models in a cloud-based ML platform. This role serves as a subject matter expert in Machine Learning Operations (MLOps), bridging the gap between data science and production-grade systems. The engineer will help shape and guide ML solutions within an evolving technology stack and will have the autonomy to recommend and implement best-practice approaches.

Required Education & Experience

• Bachelor’s degree (Master’s preferred) in Statistics, Mathematics, Computer Science, or a related quantitative field.
• 7+ years of experience in data science or a related discipline.
• 3+ years of hands-on experience with MLOps and production ML systems.
• Proven experience deploying and scaling machine learning models in production environments.
• Strong programming skills in Python and cloud automation/scripting.
• Experience with big data platforms, real-time/streaming data, and distributed or cluster computing.
• Hands-on knowledge of cloud platforms, particularly AWS.

Key Responsibilities

• Implement and operationalize data science models in a cloud-based ML platform (e.g., AWS SageMaker).
• Design and maintain systems to monitor model performance, reliability, and drift in production.
• Act as the MLOps subject matter expert, advising data scientists on model design and deployment considerations.
• Collaborate with data engineering teams to build and maintain data pipelines from enterprise data sources (e.g., Snowflake, time-series systems).
• Partner with architecture teams to ensure compute, networking, and endpoint requirements are incorporated into ML solutions.
• Stay current with emerging machine learning techniques, tools, and best practices, and apply them where appropriate.
• Work effectively within a geographically distributed team, communicating priorities and project status clearly.
• Design solutions that balance performance, scalability, and cost to meet business objectives.