Role : Technical Project Manager - AI Initiatives
Location : Charlotte, NC (Hybrid)
Overview
Drive successful delivery of AI/ML projects for a large enterprise through strong technical understanding, strategic stakeholder management, and cross-functional collaboration. Serve as the connector between technical teams, business units, and leadership to ensure AI initiatives deliver business value on time and within scope.
Key Responsibilities
Project Leadership & Delivery
- Lead end-to-end delivery of AI/ML projects from ideation through production deployment
- Develop comprehensive project plans, timelines, milestones, and resource allocation strategies
- Manage project scope, budget, risks, and dependencies across multiple concurrent initiatives
- Track and report on project health metrics, KPIs, and progress to stakeholders and leadership
- Facilitate agile ceremonies including sprint planning, stand-ups, retrospectives, and demos
- Identify and resolve blockers, escalate critical issues, and drive decisions to keep projects on track
Strategic Relationship Building
- Build and maintain strong relationships with executive sponsors, business leaders, and technical teams
- Serve as trusted advisor to stakeholders on AI project feasibility, timelines, and trade offs
- Align project objectives with broader organizational strategy and business outcomes
- Cultivate partnerships with data science, ML engineering, infrastructure, and platform teams
- Engage with external vendors, partners, and consultants to supplement internal capabilities
- Establish credibility through technical understanding and consistent delivery excellence
Cross-Functional Collaboration & Communication
- Act as central liaison connecting data scientists, engineers, product managers, and business stakeholders
- Translate technical AI/ML concepts into business language for non-technical audiences
- Communicate project status, risks, and impacts clearly across all organizational levels
- Facilitate workshops, design sessions, and alignment meetings with diverse stakeholder groups
- Navigate organizational complexity to build consensus and drive alignment on priorities
- Create and maintain documentation including project charters, status reports, and decision logs
AI/ML Domain Expertise
- Understand AI/ML workflows: data collection, model development, training, validation, and deployment
- Grasp key concepts: supervised/unsupervised learning, neural networks, LLMs, model evaluation metrics
- Comprehend infrastructure requirements: compute resources, data pipelines, MLOps tooling
- Recognize AI project risks: data quality issues, model bias, performance degradation, regulatory concerns
- Stay current on AI trends, tools, and best practices relevant to enterprise applications
- Bridge the gap between technical teams and business stakeholders through informed facilitation
Stakeholder Management
- Conduct regular stakeholder meetings to gather requirements, provide updates, and manage expectations
- Navigate competing priorities and negotiate trade-offs between speed, quality, and scope
- Build stakeholder buy-in for project decisions, approach changes, and resource needs
- Manage change requests and scope adjustments through structured governance processes
- Create executive-level presentations and dashboards highlighting business impact and ROI
- Foster collaborative environment where all voices are heard and conflicts are resolved constructively
Required Qualifications
Project Management Experience
- 5+ years of technical project management experience, preferably in AI/ML or data intensive projects
- Proven track record delivering complex technical projects on time and within budget
- Strong understanding of agile methodologies (Scrum, Kanban) and project management frameworks
- Experience managing cross-functional teams of 10+ people across multiple disciplines PMP, PMI-ACP, Certified Scrum Master, or equivalent certification preferred
AI/ML Knowledge
- Working knowledge of AI/ML concepts, workflows, and technologies
- Understanding of data science project lifecycle and common challenges
- Familiarity with ML frameworks (TensorFlow, PyTorch), MLOps tools, and cloud AI platforms
- Awareness of AI governance, ethics, model risk management, and regulatory considerations
- Ability to have technical conversations with data scientists and ML engineers
Collaboration & Communication Skills
- Exceptional communication skills with ability to tailor messages for technical and executive audiences
- Proven ability to build trust and influence stakeholders at all organizational levels
- Expert facilitator capable of driving productive meetings and workshops with diverse groups
- Strong emotional intelligence and interpersonal skills to navigate organizational dynamics
- Experience presenting to C-level executives and managing upward effectively
- Skilled at conflict resolution and building consensus across competing interests
Technical Acumen
- Comfort working in technical environments and learning new technologies quickly
- Understanding of software development lifecycle and DevOps practices
- Familiarity with cloud platforms (AWS, Google Cloud Platform, Azure) and enterprise IT infrastructure
- Ability to review technical documentation and ask informed questions
- Experience with project management tools (Jira, Confluence, MS Project, Smartsheet)
Preferred Qualifications
- Experience in banking, financial services, or other regulated industries
- Background in data engineering, analytics, or software development
- MBA or technical advanced degree
- Experience with enterprise AI platforms and governance frameworks
- Knowledge of FinOps, cost management, and ROI analysis for AI projects
- Previous consulting experience or client-facing roles
- Familiarity with change management and organizational transformation
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