• Partner with state organizations, research teams, and enterprise education clients to understand their data collection goals and visualization needs. • Lead the end-to-end onboarding process for all tutoring providers connected to your clients. • Collaborate closely with the internal data engineering team to translate client requirements into clear, technical documentation. • Own the project management lifecycle for deliverables by creating, tracking, and prioritizing tickets within Jira.

• Own assigned projects and workstreams from initiation through closeout. • Develop and maintain project execution plans, schedules, and milestone tracking. • Drive project progress and accountability across internal teams, vendors, consultants, and external partners. • Coordinate project activities to ensure alignment with program objectives, timelines, and deliverables. • Facilitate project meetings, track action items, and follow through to completion. • Build and maintain project-level schedules aligned to the broader program master schedule. • Monitor project dependencies and identify impacts to adjacent workstreams. • Support integration of project schedules into the overall program plan. • Escalate schedule risks and conflicts proactively while recommending mitigation strategies. • Coordinate activities across engineering, design, construction, infrastructure, and operational stakeholders. • Manage communication between project teams, vendors, consultants, and leadership. • Ensure all parties understand scope, responsibilities, milestones, and expectations. • Support cross-functional collaboration across the QC Ecosystem and external delivery partners. • Track project budgets, scope, schedules, risks, issues, and progress. • Maintain accurate project documentation including meeting notes, decision logs, change requests, and status reports. • Provide regular project updates and executive-ready reporting to Program Leadership. • Contribute to phase-gate reviews and readiness assessments. • Identify project-level risks, constraints, and issues early. • Drive resolution through collaboration and ownership.

• Co-own technical architecture and system design • Lead design of data and AI flows across heterogeneous systems • Make and document architectural decisions independently • Design and build MCP tools, RAG pipelines, and agent-facing APIs • Develop backend services with FastAPI • Set and uphold engineering standards across the team • Mentor engineers across the team • Own the evolution of the Data & AI domain architecture • Lead technical execution of initiatives from discovery to production • Act as the technical point of contact for initiatives within the domain • Collaborate closely with other Technical Leads and Principal Engineers • Drive engineering excellence in production

• Be responsible for the entire end-to-end lifecycle of Artificial Intelligence solutions, from design and architecture to validation, deployment and availability for consumption via APIs, ensuring scalability, stability and high performance; • Develop, evaluate and optimize Machine Learning models and Generative AI applications, ensuring the technical quality of solutions and their alignment with business needs; • Continuously analyze the trade-off between computational cost and solution performance, considering factors such as token consumption, inference time, infrastructure usage and return on investment (ROI), and propose alternatives that maximize efficiency and value for the organization; • Implement governance, security and reliability mechanisms for AI applications, using strategies such as guardrails, response validation, quality monitoring and context-anchoring techniques, including Retrieval-Augmented Generation (RAG), to ensure accurate, safe and business-aligned responses; • Develop and maintain data pipelines and inference flows in collaboration with multidisciplinary teams, ensuring integration, observability and continuous monitoring of models in production; • Document architectures, models, experiments and processes to promote traceability, reproducibility and adherence to data and AI governance practices; • Collaborate with business and technology stakeholders to identify opportunities for AI application, translating complex challenges into scalable, high-impact analytical solutions; • Stay up to date with the evolution of technologies, frameworks and best practices related to Data Science, Machine Learning and Generative AI, proposing innovations that add value to the business.