Software Engineer – Machine Learning

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.
• 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.
• 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.
• 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

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.

• 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.
• Experience: 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.

Soft Skills

• Ability to work independently and collaboratively in a fast-paced environment.
• Strong communication skills to influence stakeholders and explain technical concepts.

Nice-to-Have

• Knowledge of insurance industry data and business processes.
• In-depth knowledge in information systems and ability to identify, apply, and implement best practices.
• Understanding of key business processes and competitive strategies related to the IT function.
• Ability to plan and manage projects and solve complex problems by applying best practices.
• Ability to provide direction and mentor less experienced teammates.
• Ability to interpret and convey complex, difficult, or sensitive information.