About This Role

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 pedagogicalenvironments. 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

What You Bring

TECHNICAL

• 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

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

Compensation

At Cengage Group, we take great pride in our commitment to providing a comprehensive and rewarding Total Rewards package designed to support and empower our employees. Click here to learn more about our Total Rewards Philosophy.

The full base pay range has been provided for this position. Individual base pay will vary based on work schedule, qualifications, experience, internal equity, and geographic location. Sales roles often incorporate a significant incentive compensation program beyond this base pay range.

In this position, you will be eligible to participate in the company's discretionary incentive bonus program. This position's bonus target amount, which is not guaranteed and is dependent on individual performance and overall company results among other factors, is provided below.

15% Annual: Individual Target

$117,100.00 - $187,300.00 USD