Senior Technical Project Manager

Cambium Learning Group

About The Position

Cambium Learning Group is an award-winning educational technology solutions leader dedicated to helping all students reach their potential through individualized and differentiated instruction. Using a research-based, personalized approach, Cambium Learning Group delivers SaaS resources and instructional products that engage students and support teachers in fun, positive, safe and scalable environments. These solutions are provided through Learning A-Z® (online differentiated instruction for elementary school reading, writing and science), ExploreLearning® (online interactive math and science simulations, a math fact fluency solution, and a K–2 science solution), Voyager Sopris Learning® (blended solutions that accelerate struggling learners to achieve in literacy and math and professional development for teachers), and VKidz Learning (online comprehensive homeschool education and programs for literacy and science). We believe that every student has unlimited potential, that teachers matter, and that data, instruction, and practice are the keys to success in the classroom and beyond. We are seeking an innovative and highly proactive Senior Technical Project Manager (TPM) to manage high-impact, cross-functional AI initiatives here at Cambium Assessment. In this role, you will manage an AI roadmap and work closely with the AI Engineering Lead, including contributing to the technical roadmap, evaluating AI use cases, and managing the workload and schedule of the team.

Requirements

Bachelor’s degree in computer science, Data Science, or a related technical field with 7+ years of technical project management experience (or a Master's degree with 5+ years of experience).
Solid grasp of core AI/ML concepts (e.g., LLMs, RAG pipelines, fine-tuning vs. prompting, model evaluations) and data pipeline infrastructure.
Demonstrated ability to write and execute basic-to-intermediate SQL queries, alongside a proven ability to interpret backend web application components and system data relationships.
Comfort utilizing modern AI-assisted engineering productivity tools (e.g., Copilot, Cursor, or automated workflow agents) to accelerate delivery tracking

Nice To Haves

Prior experience deploying production-grade generative AI features, agentic workflows, or natural language processing (NLP) applications.
Exposure to incident management or high-pressure production support in cloud computing environments.

Responsibilities

Co-Manage the AI Roadmap: Work closely with the AI Engineering Lead to manage the team's long-term technical vision, evaluate emerging AI technologies, and assess the feasibility of proposed solutions.
Identify AI Opportunities: Collaborate with the team to identify areas where generative AI or predictive models can optimize systems, processes or enhance feature sets.
Triage Complex AI Systems: Troubleshoot systemic technical issues across data pipelines, backend services, and model deployment layers, understanding data dependencies to manage the engineering escalation pipeline.
Advanced Data Validation: Execute SQL queries to validate, inspect, and analyze complex training and production datasets, ensuring absolute data integrity across systems.
Manage Release Windows: Coordinate system deployments and model release cycles in close alignment with developers, DevOps, and systems engineering teams.
Support Operational Maintenance: Oversee deployment activities during maintenance windows to ensure smooth cross-system stability and timely engineering resolution if issues arise.
Drive management tracking and execution in Jira, actively managing the backlog, tracking active sprints, and reporting delivery status.
Translate complex technical findings and system constraints into clear, actionable insights for non-technical leadership.
Lead Matrixed Deliveries: Coordinate the end-to-end execution of multi-team AI initiatives, mapping out system-wide dependencies across multiple teams.
Agile Management: Drive management tracking, sprint planning, and active backlog grooming in Jira/Confluence, optimizing workflows for the unique iterative/probabilistic lifecycle of AI development.