Principal Machine Learning Engineer

Overview

Lead enterprise AI/ML architecture and build secure, scalable machine-learning and generative-AI solutions across cloud and on-premises environments. The role combines hands-on engineering, technical strategy, stakeholder partnership, and team leadership.

What you'll do

• Own enterprise AI/ML architecture, standards, APIs, and guardrails.
• Build production ML and GenAI solutions, applications, and end-to-end pipelines.
• Establish observability, service objectives, safe deployment practices, and incident runbooks.
• Lead model evaluation, drift detection, and automated retraining.
• Architect LLM and RAG systems with prompt management and safety guardrails.
• Translate domain needs into roadmaps, mentor teams, and communicate technical trade-offs.

What you'll need

• 2-12 years of Machine Learning Engineer experience depending on education level, plus 2 years of people-management or team, project, program, or resource-allocation leadership experience.
• 3-5 years of experience in AI/ML and enterprise software.
• Strong knowledge of machine-learning algorithms, deep learning, and LLM/RAG techniques.
• Proficiency in Python and Java.
• Experience with Docker/Kubernetes, cloud platforms, and DevOps/MLOps tools.
• Ability to model TCO versus NPV and present trade-offs to executives.

Nice to have

• Biotechnology or pharmaceutical industry experience.
• Published enterprise GenAI thought leadership or conference talks.
• Master’s degree in Computer Science or Data Science.
• Agile or SAFe project-delivery experience.
• Certifications on AWS, Azure, or Google Cloud AI/ML platforms.

Details

• Remote in the United States.
• Annual salary range: USD 187,395.25-253,534.75.