Software Engineer

• Design, develop, test, and maintain software applications using modern programming languages and frameworks.
• Collaborate with product managers, designers, and other engineers to translate requirements into technical specifications.
• Write clean, maintainable, and efficient code, following best practices and coding standards.
• Participate in code reviews to ensure code quality, functionality, and maintainability.
• Troubleshoot, debug, and resolve technical issues and bugs in a timely manner.
• Optimize applications for maximum performance, scalability, and reliability.
• Contribute to the continuous improvement of development processes, tools, and methodologies.
• Stay up-to-date with emerging technologies and industry trends, and apply them to improve our products and solutions.

• Apply AI-assisted engineering techniques across the full delivery lifecycle, including requirements analysis, pipeline and code development, testing, debugging, documentation, and operational monitoring.
• Iterate with AI on solution design and architecture, evaluating alternatives, revising existing architecture, and producing design artifacts for review.
• Set the standard for validating AI-generated outputs for correctness, security, maintainability, compliance, and business appropriateness, including when reviewing the AI-assisted work of other engineers.
• Design and support data platforms and governed datasets that enable machine learning, analytics, and generative AI workloads.
• Contribute reusable prompts, skills, agents, engineering playbooks, and AI-assisted development patterns that benefit the broader engineering organization.
• Ensure AI usage complies with enterprise policies for intellectual property protection, security, privacy, PHI handling, and Responsible AI standards.
  

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
• Proficiency in one or more programming languages, such as C# or Python, and relevant frameworks.
• Strong understanding of software design principles, algorithms, and data structures.
• Knowledge of cloud platforms (e.g., Azure, AWS, GCP), and experience with data engineering technologies (e.g., Spark, Databricks, Microsoft Fabric)
• Knowledge and experience with modern data engineering patterns (e.g., Data Vault, dimensional modeling, data products, lakehouse architecture, feature flags, event-driven systems, DAGs).
• Familiarity with version control systems such as Git and collaborative coding workflows.
• Ability to write unit, integration, and end-to-end tests to ensure code quality and reliability.
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Strong communication skills with the ability to articulate technical concepts to both technical and non-technical stakeholders.
• Demonstrated experience using AI-assisted development platforms such as GitHub Copilot, Cursor, Claude Code, or similar tools to deliver production work.
• Strong prompt engineering skills, with experience in AI-assisted testing and AI-enabled delivery practices.
• Experience with machine learning data pipelines, vector search technologies, document processing pipelines, or AI data preparation techniques.Working knowledge of data governance, lineage, quality, and observability practices supporting AI-enabled solutions.