Machine Learning Engineer

Machine Learning Engineer

We are building the foundation of the machine learning function at a market-leading insurance company. As one of the first data science hires, you will play a pivotal role in shaping our ML strategy, frameworks, and operating model. This is a unique opportunity to be hands-on, developing and maintaining production-grade solutions while influencing the long-term vision and scaling of our ML capabilities.

Key Responsibilities

• Partner with the Head of ML, product and data teams to define and implement the company-wide ML framework and best practices.
• Contribute to the roadmap for ML development, adoption and team growth.

Hands-On Development

• Ideate, design and build ML and AI prototypes to validate priority use cases and solve complex business problems to drive tangible value, while collaborating with product and business teams.
• Develop and deploy production-grade ML models and data pipelines.
• Build orchestration and integration frameworks for ML models and pipelines.
• Develop and maintain CI/CD pipelines for ML solutions, including test automation, to ensure successful deployment of updated models

Operational Excellence

• Monitor, maintain, and retrain models in production to ensure performance and compliance.
• Manage data updates, versioning, and integrity for deployed solutions.
• Implement robust monitoring and alerting systems for ML services.

Team Building

• Help establish processes, tools, and standards for a growing ML team.
• Mentor future hires and contribute to a collaborative, innovative culture.

Qualifications

Required Qualifications: The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Bachelor's Degree and six to ten years of experience or equivalent education and software engineering training or experience
• Strong proficiency in Python and ML frameworks.
• Experience with Databricks and Azure for data engineering and ML workflows.
• Familiarity with MLOps tools (MLflow, Lakehouse Monitoring, Azure DevOps) and CI/CD practices.
• Solid understanding of data science and engineering principles and model lifecycle management.
• 5+ years total in data analytics, infrastructure, engineering and science roles.
• 2+ years in applied ML engineering or data science roles.
• Proven track record of deploying ML models into production environments.
• Familiarity with monitoring, retraining, and maintaining ML systems at scale.
• Ability to work independently and collaboratively in a fast-paced environment.
• Strong communication skills to influence stakeholders and explain technical concepts.

Location: Charlotte, Dallas, Raleigh, Alpharetta