Principal Machine Learning Engineer

About FM:

FM is a 190-year-old, Fortune 500 commercial property insurance company of 6,000+ employees with a unique focus on science and risk engineering. Serving over a quarter of the Fortune 500 and major corporations globally, they deliver data-driven strategies that enhance resilience, ensure business continuity, and empower organizations to thrive.

FM India located in Bengaluru is a strategic location for driving FM's global operational efficiency that allows them to leverage the country's talented workforce and advance their capabilities to serve their clients better.

Principal Machine Learning Engineer

Job Summary:

This role plays a pivotal role in the Data Science team, responsible for designing, developing, and deploying advanced machine learning solutions that address complex business challenges in the

property insurance domain. This role emphasizes end-to-end technical ownership of ML projects, from ideation through production, and requires close collaboration with cross-functional teams including Data Scientists, ML Ops Engineers, and Data Engineers. The role also contributes to mentoring junior team members to foster technical excellence and innovation.

Essential Functions & Responsibilities:

Description:

Model Engineering & Deployment:

• Design, build, and optimize machine learning models for production use. Ensure models are scalable, reproducible, and maintainable across environments.

MLOps & Infrastructure:

• Lead the implementation of MLOps best practices including version control, CI/CD, model monitoring, and cloud deployment (e.g., Databricks, MLflow).

Cross-functional Collaboration:

• Work closely with Data Scientists, Scientists, Product Managers, and Engineering teams to translate business needs into technical solutions. Ensure seamless integration and monitoring of ML models in production.

Mentorship & Technical Leadership:

• Provide guidance and coaching to junior engineers and data scientists. Promote knowledge sharing and uphold high standards in ML engineering practices. machine learning and AI, supporting internal consulting and knowledge sharing.

Quality Assurance & Support:

• Conduct rigorous testing and validation ofmodels. Monitor performance in production and respond to support tickets and operational issues.

Minimum Qualifications:

• Except where required by licensure or regulation a combination of comparable education and experience may be used to satisfy qualification requirement.

Skills:

• Advanced proficiency in Python or R for machine learning and data science.
• Hands-on experience with ML platforms such as Databricks and MLflow.
• Strong understanding of MLOps practices including CI/CD, model versioning, and monitoring.
• Familiarity with cloud-based deployment and infrastructure (e.g., Azure, AWS).
• Experience with model testing, validation, and performance tuning.
• Proficiency in version control systems (e.g., Git) and collaborative development workflows.
• Ability to translate business problems into scalable ML solutions.
• Strong communication and collaboration skills for cross-functional teamwork.
• Experience mentoring junior engineers and promoting best practices in ML engineering.

Education:

Minimum Education Required to Perform Essential Job Functions:

• 4 Year / bachelor's degree

Specific Degree, if required (ex - Engineering, Juris Doctorate, etc):

• Engineering or scientific discipline, e.g. Data Science, Machine Learning, Computer Science, Software Engineering, or Statistics.

Experience:

• Minimum Years of Experience Required to Perform Essential Job Functions: 8

Additional Experience Qualifier (optional):

• Graduate degree included and preferred. 3+ years of experience leading ML initiatives or teams. Proven track record of deploying ML models in production environments using tools like MLflow and platforms such as Databricks.