Sr Software Engineer

Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We collaborate with education organizations to develop strategies and implement solutions that enable the transformative change our clients need to own their future. Together, we empower clients to drive innovation, create sustainable funding streams and deliver better student outcomes.

Today, leaders of higher education institutions and academic medical centers are spending too much time reacting to market forces, rather than focusing on the essential priorities that help students, faculty and staff thrive. The most productive path forward requires an intentional approach and innovative thinking, whereby stakeholders across the entire institution rally around a shared vision and embrace the hard work of effecting change.

You’ll help our clients to achieve organizational effectiveness, improve student outcomes, implement new technologies and align resources and investments to ensure long-term sustainability.

Join our team as the expert you are now and create your future.

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As a Senior Software Engineer at Huron, you will lead the design, development, and delivery of enterprise software products built on the Huron platform. You will apply deep engineering expertise to build scalable, AI-augmented applications, mentor engineers, and partner closely with Product, Implementation, and Support teams within Huron's Higher Education practice.

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Primary responsibilitiesArchitecture and technical leadership

· Lead design reviews, and drive technical decisions on scalability, reliability, and maintainability.

· Translate product requirements into clean, well-tested implementations; author architecture decision records (ADRs) and developer documentation.

· Define and govern engineering quality standards, coding conventions, and peer review norms across the team.

AI-assisted and agentic development

· AI-assisted development: Leverage GitHub Copilot, Claude Code, and comparable agentic coding assistants across code authoring, review, test generation, and documentation to increase quality and velocity.

· AI agent development: Design, build, and maintain AI agents and multi-agent workflows; define agent instructions, skills, memory, and tool integrations; evaluate agent behavior in production.

· MCP and tool integrations: Apply Model Context Protocol (MCP) or equivalent patterns to connect AI models to internal tools, APIs, and data sources for agentic workflows.

· Stay current with the agentic AI landscape; bring forward-looking proposals to the engineering team.