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.

Essential Duties and Responsibilities

• Develop customized coding, software integration, perform analysis, configure solutions, using tools specific to the project or the area.
• Lead and participate in the development, testing, implementation, maintenance, and support of highly complex solutions in adherence to company standards, including robust unit testing and support for subsequent release testing.
• Build non-functional monitoring capabilities and provide escalated support for highly complex applications in production.
• Build in and maintain security controls and monitoring in support of company standards.
• Typically lead moderately complex projects and participate in larger, more complex initiatives.
• Solve complex technical and operational problems. Act as a resource for teammates with less experience.
• May oversee the work of a small team.
• In an Agile environment: Responsible for delivering high quality working software and automating manual/reusable tasks working directly, and engage with, the business from the beginning of the design work. Leverage continuous engineering practices to deliver business value regarding effectiveness of the design. Actively participate in refining user stories. Responsible for design, developing, and maintaining automated unit testing, and supporting integration and functional testing. Responsible for providing automated monitoring capabilities, providing warranty support, and providing knowledge transfer to production support. Develop code in accordance with the acceptance criteria established by the Product Owner.

Qualifications

• 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