[Hiring] Forward Deployed Engineer - AI/ML Data Science @Cengage Group

Role Description

Cengage is at an inflection point. As we scale our AI-powered learning ecosystem including Student Assistant, AI faculty insights, and Cengage Unlimited, the gap between a polished platform demonstration and a deeply embedded, outcomes-driving deployment at an institution is where the real work lives. The Lead Field Development Engineer closes that gap.

As a Lead FDE, you will embed directly with Cengage's most strategic institutional partners to architect, configure, and ship production-grade AI and platform solutions tailored to their academic, compliance, and pedagogical environments. This is not a sales engineering role: you will write and own production code, influence Cengage's core platform roadmap with field-derived insights, mentor other engineers, and establish the standard for complex institutional AI deployments.

What You'll Own

• STRATEGIC INSTITUTIONAL DEPLOYMENT
  • 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.
• TECHNICAL ARCHITECTURE & ENGINEERING
  • 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.
• LEADERSHIP & ENABLEMENT
  • 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.

What You'll Build in Your First 12 Months

• 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.

Qualifications

• 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.
• 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.

Leadership & Communication

• 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.

Preferred Qualifications

• 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.