Forward Deployed Engineer - AI/ML Data Science

Cengage Group•Virtual US OH, OH

13d•$117,100 - $187,300•Hybrid

About The Position

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. 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.

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

Nice To Haves

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

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