Senior Project Manager - AI & Personalization Platform

About the job

Our client is seeking a Senior Project Manager - AI & Personalization Platform for a 12+ month fully onsite contract in Irving, TX! No C2C!

Required Skills & Experience

• PMP certification from the Project Management Institute required.
• 7+ years of project or program management experience delivering technical, software, data, platform, AI/ML, or digital initiatives.
• Demonstrated experience leading cross-functional delivery across product, engineering, data, architecture, operations, and business teams on concurrent, interdependent initiatives.
• Experience delivering or operating AI/ML, personalization, recommendation, decisioning, experimentation, or advanced analytics platforms; direct model development is not required.
• Working knowledge of the AI/ML and platform delivery lifecycle, including data readiness, solution development, evaluation, deployment, observability, monitoring, iteration, and retirement.
• Practical understanding of AI and personalization risks and controls, including privacy, security, bias and fairness, explainability, human oversight, data governance, and model or solution performance.
• Experience managing platform initiatives with APIs, integrations, services, data pipelines, cloud infrastructure, reliability requirements, or operational support models.
• Hands-on experience with Agile/Scrum delivery as well as hybrid and waterfall approaches, and with delivery tooling such as Jira, Confluence, Smartsheet, or MS Project.
• Proven track record of dependency, risk, issue, vendor, and stakeholder management on complex, multi-team programs.
• Strong executive communication and status-reporting skills, with the ability to make AI concepts, platform dependencies, uncertainty, and trade-offs clear to both technical and business audiences.
• Bachelor’s degree or equivalent practical experience.

Desired Skills & Experience

• Experience with generative AI, large language models, retrieval-augmented generation, AI agents, prompt evaluation, content generation, or conversational AI platforms.
• Experience delivering recommendation systems, next-best-action, ranking, propensity, customer segmentation, profile, decisioning, or real-time personalization capabilities.
• Familiarity with MLOps, model monitoring, feature stores, data quality, experimentation platforms, model registries, evaluation frameworks, or feature and decisioning services.
• Familiarity with cloud-native platforms, APIs, event-driven architectures, streaming data, microservices, observability, platform reliability, and infrastructure-as-code.
• Experience establishing responsible-AI governance, model-risk processes, privacy reviews, security reviews, or enterprise AI standards.
• Experience managing platform modernization, technical-debt reduction, scalability improvements, performance optimization, or reliability programs.
• Additional certifications such as PMI-ACP, CSM/PSM, SAFe, or an AI/ML, product, cloud, or data certification.
• Experience managing build-versus-buy evaluations and vendor-delivered AI, personalization, data, or platform workstreams.
• Retail, convenience-retail, loyalty, consumer technology, or customer-experience platform experience.

What You Will Be Doing

• Own end-to-end delivery of AI and Personalization Platform initiatives, including problem definition, scope, integrated plans, schedules, risks, dependencies, stakeholder communication, launch readiness, and post-launch measurement.
• Build and maintain an integrated platform roadmap, sequencing foundational capabilities, product increments, technical enablers, and enterprise dependencies.
• Translate platform strategy, business needs, technical assessments, and prioritized opportunities into milestone-based delivery plans with clear owners, timelines, dependencies, and success criteria.
• Establish and run a clear intake and prioritization process for AI and personalization platform requests, balancing strategic value, customer impact, data readiness, technical feasibility, capacity, cost, and risk.
• Coordinate delivery across product, AI/ML engineering, software engineering, data engineering, data science, architecture, platform operations, QA, privacy, security, legal, and vendor teams.
• Drive the AI and personalization delivery lifecycle from discovery and data readiness through solution design, development, evaluation, testing, deployment, monitoring, and continuous improvement.
• Coordinate platform capabilities such as data pipelines, feature and profile services, model services, decisioning, recommendation and ranking, experimentation, APIs, integrations, observability, and operational tooling.
• Partner with product and technical leaders to define acceptance criteria and evaluation plans covering relevance, accuracy, quality, latency, scalability, reliability, safety, fairness, explainability, cost, adoption, and business outcomes as appropriate.
• Establish launch-readiness practices for platform releases, including technical validation, performance testing, integration testing, operational readiness, documentation, support ownership, and rollback planning.
• Coordinate monitoring and continuous-improvement plans for production AI and personalization capabilities, including model drift, data quality, performance degradation, service health, unexpected behavior, and changing business conditions.
• Establish practical governance for AI and personalization delivery, including privacy, security, responsible-AI controls, data lineage, model or solution documentation, human oversight, approval checkpoints, and auditability.
• Run Agile delivery cadences within a product and engineering operating model, including planning, backlog refinement, standups, reviews, retrospectives, roadmap reviews, and release checkpoints.
• Proactively manage risks, issues, assumptions, dependencies, and decisions (RAID); remove blockers and escalate early with recommended options and trade-offs.
• Coordinate build-versus-buy evaluations and vendor/SOW workstreams for AI platforms, personalization services, data products, experimentation tools, and supporting technologies.
• Track staffing, including FTE and contractor capacity, budget, platform consumption, vendor spend, and resourcing against plan.