Machine Learning Engineer

Overview

Principal Machine Learning Engineer to design, build, deploy, and scale machine learning and generative AI systems for production products across multi-cloud environments.

What you'll do

• Design, develop, and deploy ML and LLM-based solutions for production use cases.
• Collaborate with Generative AI Center of Excellence and business stakeholders on buy-vs-build decisions.
• Build and integrate agent-based workflows using Google Agentspace, Microsoft Copilot, and Salesforce Agentforce.
• Develop end-to-end ML pipelines from data ingestion through monitoring.
• Architect and implement LLM-powered systems integrating agents and services across GCP, AWS, and Azure.
• Optimize ML workflows for performance, scalability, reliability, and cost efficiency in cloud environments.
• Implement MLOps best practices including CI/CD, model versioning, experiment tracking, and automated retraining.

What you'll need

• PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years experience in ML, CS, Data Science, or related fields.
• Strong proficiency in Python for machine learning and production systems.
• Solid understanding of software engineering fundamentals, system design, and design patterns.
• Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS).
• Experience building and deploying production-grade ML systems.
• Strong communication skills to explain technical concepts to technical and non-technical stakeholders.
• Excellent time management, collaboration, and organizational skills.