Operations & Project Management

Senior Project Manager - AI & Personalization Platform

Motion Recruitment Partners, LLCIrving, TX🇺🇸United StatesPosted Sep 18, 2026

Why This Role Stands Out

This role offers a fantastic opportunity to lead critical AI and personalization platform initiatives within a reputable company, developing your expertise in cutting-edge technology. You'll thrive here if you have a strong PMP-certified background in technical project management and enjoy collaborating with diverse, cross-functional teams to drive complex projects to successful completion. We encourage you to apply and explore this exciting path for professional growth.

Quick Overview

Seniority

Mid Senior

Work mode

On Site

Location

Irving, TX, United States

Posted

Yesterday

SAFeScrumAgileConfluenceContinuous ImprovementJiraPMPStakeholder ManagementWaterfall

Job Description

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.