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LSAC's mission is to advance law and justice by promoting access, equity, and fairness in law school admission and supporting the learning journey from prelaw through practice.
Pay rate: $126,000 to $135,000 per year, depending on experience
We are seeking an Engineer Lead with Generative AI experience. You will work as part of an autonomous agile team to develop features and applications to meet the needs of the Technology Solutions and Platforms department.
We are looking for a candidate with a deep understanding of engineering best practices and a strong agile mindset. As lead, you are responsible for ensuring the team's alignment with best practices, enterprise architecture, and stakeholder satisfaction. In addition, this position is responsible for operational maintenance, troubleshooting, and support of applicable back-end systems.
We rely heavily on web-based services, and you will have the opportunity to work with new and interesting technology.
This role carries an expanded mandate to design, prototype, and deliver Generative AI solutions that directly enhance LSAC products and internal tooling. LSAC currently uses GitHub Copilot as its primary AI-assisted development tool; this role will own that practice and grow it—expanding our AI capabilities into large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and responsible AI practices. You will partner with the Software Development Manager (SDM), data engineers, and stakeholders to identify high-value AI opportunities and bring them to production.
Equally critical is the human side of this transformation. This person will be a hands-on change agent for Generative AI adoption across the engineering organization—translating emerging AI capabilities into practical, day-to-day habits for developers, testers, and product teams. You will design and run enablement programs, lead by example in code, and build the organization's confidence and fluency with AI tooling from the ground up.
Essential Job Functions
Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions of this position. The individual employed in this position will be required to:
- Collaborate with partners within the company to design, configure, maintain, and promote a variety of internally- and externally facing applications.
- Collaborate across areas to ensure application reliability and coding to architectural standards.
- Support product teams by advocating for their needs and providing constructive guidance.
- Provide the glue across dependent teams to ensure technical dependencies are planned for and addressed in a fast-paced environment.
- Connect with developers within your team to coach, inspire, and foster a continuous learning environment.
- Build and maintain scalable full-stack applications.
- Continuously monitor, test, and optimize software.
- Collaborate with software engineers, the Software Development Manager (SDM), analysts, and stakeholders to deliver solutions that meet or exceed customer expectations.
- Contribute as part of the team by leveraging continuous delivery methods and test-driven development to frequently deliver new functionality.
- Deliver high-quality code and hold the team to the same standard through code review and mentoring.
- Work to scale software to support dynamic teams in a fast-paced environment.
- Be a contributor of knowledge to the team by reviewing code, sharing experience, and listening.
- Champion and govern team-wide use of GitHub Copilot establish prompting standards, review AI-generated code for security and quality, track productivity metrics, and coach developers on effective, responsible use of AI-assisted development.
- Drive organizational Gen-AI adoption: build and execute a structured enablement roadmap that moves the engineering organization from awareness to competency, delivering workshops lunch-and-learns, hands-on labs, and reference implementations that make AI tooling tangible and accessible for developers at all levels.
- Coach and mentor engineers on Gen-AI practices, provide 1:1 and team-level guidance on integrating AI into daily workflows including AI-assisted coding, AI-augmented code review, AI-accelerated test generation (unit, integration, regression), and effective use of AI in debugging and documentation. Help engineers build critical evaluation skills for vetting AI-generated output.
- Act as internal AI change agent. Identify adoption blockers, measure team AI maturity over time, gather feedback from engineers and stakeholders, and continuously iterate on enablement programs.