Senior Project Manager - AI |(W2) | Irving, TX (5 days onsite)

Hi,

Hope you are doing well!

I'm Shikha Dixit, Lead Recruiter at Siri Info Solutions. I've reviewed your background in [Program management, AI, ML.], and your profile stands out as an excellent match for a "Senior Project Manager AI " opening in Irving, TX. This is a high-priority , 5-day on-site role with one of our key clients . Given your experience, I'd love to discuss the details and see if this aligns with your career goals. Are you open to a quick 5-minute chat?

Role: Senior Project Manager AI (W2)

Duration: 12 months

Location: Irving, TX (5 days onsite)

Job Description:

Summary

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.

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.

Key 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.

Required Qualifications (Non-Negotiable)

• 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.

Preferred Qualifications

• 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 success looks like

• The AI and Personalization Platform roadmap is prioritized, transparent, and connected to measurable customer, platform, and business outcomes.
• Platform initiatives move predictably from intake through production launch with clear ownership, evaluation criteria, governance, and decision points.
• Core platform capabilities are scalable, reliable, observable, secure, and ready for adoption by downstream product and engineering teams.
• Production AI and personalization services are monitored and improved using agreed quality, relevance, adoption, operational, and financial measures.
• Stakeholders have a shared view of delivery status, risks, dependencies, capacity, platform costs, and expected value.
• Teams use repeatable delivery and governance practices that enable responsible innovation without compromising privacy, security, reliability, or customer trust.

Best Regards,

Shikha Dixit | Email: View email address on us.fitly.work | LinkedIn:

Lead Recruiter

Siri InfoSolutions Inc, 3 Ethel Rd, Suite # 302, Edison NJ 08817.