Project Manager

About the Company

About the job You will join a multidisciplinary AI and Personalization Platform organization spanning product management, AI/ML engineering, software engineering, data engineering, data science, architecture, platform operations, quality assurance, privacy, security, and external partners.

About the Role

Role: AI-Project manager Location : Irving, TX Onsite job --- w2 As Senior Project Manager, you will own end-to-end delivery for the AI and Personalization Platform roadmap. You will coordinate initiatives across platform foundations, data and feature capabilities, model and decisioning services, personalization services, experimentation, APIs, integrations, observability, and governance. You will translate platform strategy into actionable delivery plans, establish clear intake and prioritization, manage dependencies across technical teams, and keep leadership informed through concise, decision-ready reporting. You will help the organization deliver AI and personalization capabilities that are scalable, reliable, measurable, responsible, and ready for enterprise adoption.

Responsibilities

• 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.

Qualifications

• 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.

Required Skills

• Project or program management for technical, software, data, platform, AI/ML, or digital initiatives.
• Cross-functional delivery leadership across product, engineering, data, architecture, operations, and business teams.
• Experience with AI/ML, personalization, recommendation, decisioning, experimentation, or advanced analytics platforms.
• Knowledge of AI/ML and platform delivery lifecycle, including data readiness, solution development, evaluation, deployment, observability, monitoring, iteration, and retirement.
• 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 with APIs, integrations, services, data pipelines, cloud infrastructure, reliability requirements, or operational support models.
• Hands-on experience with Agile/Scrum, hybrid, and waterfall delivery approaches.
• Experience with delivery tooling such as Jira, Confluence, Smartsheet, or MS Project.
• Dependency, risk, issue, vendor, and stakeholder management on complex, multi-team programs.
• Executive communication and status-reporting skills.

Preferred Skills

• Experience with generative AI, large language models,