Forward Deployed Engineer - AI/ML Data Science

We believe in the power and joy of learning

At Cengage, our employees have a direct impact in helping learners around the world discover the power and joy of learning. We are bonded by our shared purpose – driving innovation that helps millions of learners improve their lives and achieve their dreams through education.

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

Cengage is committed to working with broad talent pools to attract and hire strong and most qualified individuals. Our job applicants are considered regardless of any classification protected by applicable federal, state, provincial or local laws.

Cengage is also committed to providing reasonable accommodations for qualified individuals with disabilities including during our job application process. If you are an applicant with a disability and require reasonable accommodation in our job application process, please contact us at accommodations.ta@cengage.com .

About Cengage

Cengage, a global education technology company serving millions of learners, provides affordable, quality digital products and services that equip students with the skills and competencies needed to be job ready. For more than 100 years, we have enabled the power and joy of learning with trusted, engaging content, and now, integrated digital platforms. We serve the h