Forward Deployed Engineer - AI/ML Data Science

Cengage is a global education technology company serving millions of learners, committed to driving innovation in education. They are seeking a Forward Deployed Engineer specializing in AI and ML Data Science to embed with strategic institutional partners, architect, configure, and deliver AI-powered solutions tailored to educational environments.

Responsibilities:

• Embed with 3–5 strategic institutional accounts at a time, working directly with partners to understand instructional workflows, legacy LMS architectures, and institutional data environments before proposing a solution
• Lead end-to-end delivery of MindTap AI, WebAssign, Cengage Unlimited, and custom GenAI integrations from discovery through production launch and ongoing iteration
• Design and build institution-specific configurations including adaptive learning paths, RAG-backed course assistants, and auto-graded problem banks that address pedagogical challenges at scale
• Drive LTI 1.3 and LTI Advantage integrations between Cengage platforms and institutional LMS environments such as Canvas, Blackboard, D2L, and Moodle, including SSO, grade passback, and data flows
• Write production-quality code in Python, JavaScript/TypeScript, and SQL to build integration middleware, data pipelines, and custom tooling that extend Cengage's core platforms
• Architect and deploy agentic AI workflows using LLM APIs and retrieval-augmented generation pipelines grounded in institutional course content
• Build and maintain automated evaluation frameworks that measure the accuracy, safety, and pedagogical quality of AI-generated student guidance at the institution level
• Ensure deployments meet FERPA, WCAG 2.1 AA accessibility, institutional data-governance requirements, and Cengage's AI safety standards
• Translate field-derived deployment patterns, integration heuristics, and failure modes into first-class contributions to Cengage's product and engineering roadmap
• Serve as the technical authority for field deployment practices, establishing standards, reusable integration templates, and a shared knowledge base of institutional patterns
• Mentor junior and mid-level FDEs and conduct technical reviews of deployment architectures, code, and stakeholder communication
• Partner closely with Cengage product managers, platform engineers, content teams, Sales, and Customer Success to prioritize roadmap features and define technical success criteria
• Present deployment architecture, outcomes data, and AI safety posture to institutional CIOs, Chief Academic Officers, and VP-level stakeholders with authority and clarity
• Define adoption milestones and renewal-driving outcomes for strategic accounts, ensuring technical delivery translates into measurable institutional value
• A reference deployment architecture for Cengage AI and LTI 1.3 integration that can serve as the team standard across institutions
• Custom RAG-powered course-assistant deployments embedded inside MindTap for strategic university partners, with measurable engagement and learning-outcome targets
• An automated AI evaluation harness for Cengage Student Assistant covering accuracy, academic-integrity safety, and response quality across FDE-managed accounts
• A faculty analytics integration layer connecting Student Assistant interaction data to institutional LMS gradebooks and early-alert systems
• A library of reusable integration modules for Canvas, Blackboard, D2L, and Moodle that reduces institutional onboarding time from weeks to days

Requirements:

• 7+ years of software engineering experience with a track record of shipping production systems in complex, customer-facing environments
• 3+ years in a customer-embedded or field-facing engineering role such as FDE, Solutions Engineer, Applied AI Engineer, or Implementation Architect, with ownership of full deployments rather than demonstrations along
• Strong full-stack engineering skills, including Python, JavaScript/TypeScript, REST or GraphQL API design, and modern application frameworks
• Hands-on experience building and deploying LLM-based applications in production, including RAG pipelines, prompt engineering, tool-calling agents, and evaluation frameworks
• Demonstrated experience with LMS integration standards such as LTI 1.3, LTI Advantage, AGS, NRPS, and Deep Linking
• Proficiency with cloud platforms; AWS is preferred, with experience across services such as Lambda, ECS or EKS, RDS or Aurora, S3, API Gateway, and CloudWatch
• Working knowledge of learning analytics standards such as xAPI or Caliper and educational data-privacy frameworks including FERPA, COPPA, and applicable state requirements
• Demonstrated ability to translate ambiguous institutional requirements into a concrete technical plan, own the plan end to end, and remain accountable for outcomes
• Experience presenting technical architecture and AI product strategy to C-suite and senior academic leadership, with credibility in both engineering and executive settings
• Track record of mentoring engineers and raising the technical bar of a team, not only executing individual work
• Comfort with up to 30% travel to institutional partner sites throughout the academic year
• Experience in higher education technology, edtech, or academic publishing, including an understanding of how universities procure, adopt, and measure learning technology
• Familiarity with adaptive learning platforms, learning engineering, and learning-science research
• Experience with enterprise AI governance frameworks, responsible AI evaluation, and AI safety in production deployments
• Contributions to open-source projects, published technical writing, or conference presentations related to AI deployment, platform engineering, or edtech
• AWS Certified Solutions Architect, Google Cloud Professional Machine Learning Engineer, or an equivalent certification
• Graduate degree in Computer Science, Data Science, Educational Technology, or a related field