Forward Deployed Engineer - AI/ML Data Science

About this role

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 i

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