Ellucian is a global market leader in education technology. We power innovation for higher education, partnering with more than 2,800 customers across 50 countries and serving over 20 million students. Ellucian's AI-powered platform, trained on the richest dataset available in higher education, drives efficiency, personalized experiences, and strengthened engagement for all students, faculty and staff.

Fueled by decades of experience with a singular focus on the unique needs of learning institutions, the Ellucian platform features best-in-class SaaS capabilities and delivers insights needed now and into the future. These solutions and services span the entire student lifecycle, from student recruitment, enrollment, and retention to workforce analytics, fundraising, and alumni engagement.

Ellucian's innovative solutions, vast ecosystem of partners, and user community of more than 45,000 provide best practices leading to greater institutional success and achieving better student outcomes.

Ellucian is seeking a highly skilled and innovative Lead Software Engineer to join various teams focused on developing new cloud-based, SaaS higher education solutions. This hands-on role involves full-stack development, technical leadership, and collaboration within agile environments to deliver scalable, secure, and high-quality products. As a Lead Software Engineer, you will take on pivotal roles in driving complex and impactful projects, leveraging cutting-edge technologies and leading the full software development lifecycle. This role offers opportunities to spearhead technical initiatives, mentor engineers at all levels, and work closely with internal product managers, experienced engineers, and cross-functional teams to design, develop, test, and deliver advanced features that enhance student success and drive higher education transformation.

Where You Will Make an Impact

• Technical Leadership and Delivery: Lead the design, development, delivery, and maintenance of complex SaaS solutions and large ERP systems, providing technical direction and accountability for high-quality outcomes
• Full-Stack Engineering: Engage in full-stack development, including advanced front-end frameworks (e.g., React, HTML5, TypeScript) and robust back-end APIs using languages such as JavaScript/TypeScript (Node.js), C# or Java.
• Strategic Planning: Collaborate with product leadership to define technical strategies and roadmaps. Ensure alignment with business goals and objectives.
• Quality Assurance: Advocate and implement best practices in agile software development, continuous integration/delivery (CI/CD), and DevOps processes. Ensure high-quality, scalable code, unit and functional tests, and comprehensive developer documentation (API docs, usage notes).
• Collaboration: Work closely with product managers, analysts, and team members to clarify complex requirements and ensure robust, future-proof designs. Facilitate cross-functional collaboration to drive project success.
• AI Platform and Agentic Engineering: Lead the design and implementation of AI-enabled engineering solutions, including LLM-backed workflows, agentic systems, tool-calling patterns, and model evaluation approaches that improve developer productivity, product capabilities, and operational efficiency.
• Engineering Context and Standards: Create and maintain the architectural guidance, coding patterns, domain context, review criteria, and team conventions needed for engineers and AI tools to produce consistent, maintainable outcomes.
• Ownership and Accountability: Take ownership of critical tasks from specification review through final testing and deployment, ensuring timely and high-quality delivery. Manage project timelines and deliverables.
• Problem Solving and System Analysis: Lead investigation of complex functional, integration, scalability, reliability, and performance issues through research, proof of concept, data analysis, and pragmatic technical decision-making.
• Platform and Tooling Solutions: Design, develop, test, and deliver new platform and tooling solutions using modern technologies and microservices architecture.
• Team Development: Mentor engineers at all levels through design reviews, code reviews, technical coaching, and practical guidance on engineering quality, AI-assisted development, and sound decision-making.
• Continuous Improvement: Drive adoption of better tools, practices, and technologies that improve product quality, operational efficiency, customer experience, and team effectiveness.

What You Will Bring

• Experience: 7+ years of professional software development experience in a product or SaaS environment.
• Programming Languages: Proficiency in multiple programming languages such as Node.js, JavaScript/TypeScript (React.js), Python, C#, Java, .NET Core, and related frameworks.
• Distributed Systems: Experience designing, building, and evolving large-scale distributed systems, microservices, event-driven architectures, and highly available production services.
• Cloud and Platform Engineering: Strong knowledge of cloud platforms such as AWS, containerization, infrastructure automation, and modern operational practices for reliability, scalability, and cost efficiency.
• Engineering Excellence: Deep experience with CI/CD, code review, test automation, observability, release engineering, and DevOps practices in modern software delivery environments.
• Security and Risk Awareness: Strong understanding of authentication, authorization, encryption, secure design, and practical application security principles in enterprise software.
• LLM and Agentic Systems Experience: Demonstrated experience building or guiding solutions that use large language models, including agent-based workflows, prompt and context design, tool orchestration, structured outputs, and human-in-the-loop validation patterns.
• Model Selection and Evaluation: Experience working with multiple LLMs or AI providers and making practical tradeoff decisions across model quality, latency, cost, reliability, safety, and enterprise suitability.
• AI Engineering Practices: Understanding of core patterns for production AI systems such as retrieval-augmented generation, evaluation frameworks, observability, prompt management, guardrails, fallback strategies, and secure handling of sensitive data.
• Judgment and Governance: Ability to evaluate AI-generated output critically, define validation standards, identify risk areas, and maintain human accountability for technical decisions and delivered outcomes.
• Technical Communication: Excellent communication, collaboration, and organizational skills, with the ability to align engineers, product partners, and stakeholders around technical direction and execution tradeoffs.
• Mentorship and Influence: Proven experience mentoring engineers, leading technical discussions, raising engineering standards, and shaping development practices across a team or organization.