Senior Software Engineer [Boston]

Successfully contributed to high-stakes consulting engagements from discovery to production. Written production-quality code using a variety of languages and frameworks, that include: Python, Java, JavaScript, TypeScript, React, Ruby, Scala, R, SQL, and Go. Built high-quality, maintainable software collaboratively, incrementally, and through an approach tailored towards the unique needs of the clients served. Leveraged AI-assistive development tools for tasks like scaffolding, refactoring, debugging, test generation, and documentation, where appropriate. Experience building or integrating AI/ML-powered features into products or systems (e.g., recommendation engines, NLP models, computer vision, predictive analytics). Used context-appropriate automated testing to inform software design choices and catch bugs. Debugged and refactored pre-existing code to incrementally improve it. Collaborated with teams using Git, MacOS/Linux, and CI/CD. Remedied architecture-level concerns such as scalability, security, reliability, and performance. Contributed to forums that encourage idea sharing, collaboration, and problem solving. Made thoughtful recommendations and set direction in the face of ambiguity. Mentored and shared knowledge with colleagues. Actively sought out and effectively given feedback. Communicated empathically with stakeholders and colleagues. Influenced stakeholders to balance technical considerations with product and user needs. MLOps: including tools like LangChain for application development and managing the ML lifecycle. Data Engineering: including platforms like Databricks for data processing and analytics. DevSecOps & DevOps. Infrastructure as Code.

Make immediate contributions to projects, rapidly building trust with clients and team. Work pragmatically to balance technology choices while delivering high-quality work within deadlines. Contribute to architecture and software design discussions. Collaborate on systems that involve the full ML lifecycle from data ingestion and preprocessing, to model deployment, integration, and performance monitoring. Generating new potential solutions to challenges (technical and non-technical) in partnership with team members and clients. Contribute to formal feedback conversations with teams and clients via retrospectives. Clearly articulate and document outcomes and drive forward action items. Regular syncs with delivery team for status updates, risk identification, evaluation, and potential solutions. Identify, mitigate, and resolve blockers to team progress. Pursue opportunities to further skills for career growth and to keep pace with technology changes.