Smart Source Technologies

Location: Hybrid (3 days onsite)New York, NY office or Stamford, CT office

.

The AI Project Manager is responsible for leading the planning, execution, and delivery of Artificial Intelligence (AI), Generative AI, machine learning, and intelligent automation initiatives across the organization. This role serves as the bridge between business stakeholders, technology teams, data scientists, and governance functions to ensure AI solutions deliver measurable business value while meeting risk, compliance, and operational standards.
The successful candidate will drive AI programs from ideation through implementation, establish delivery frameworks, manage project portfolios, and ensure successful adoption of AI-powered capabilities that enhance productivity, decision-making, customer experience, and operational efficiency.
Role Mandate
Translate AI ideas into governed, scalable, adopted solutions that deliver measurable productivity, decision quality, and operating efficiency outcomes.
Key Responsibilities
AI Program & Project Leadership
Lead end-to-end delivery of AI, Generative AI, machine learning, and automation initiatives.
Develop project plans, timelines, budgets, resource requirements, and execution strategies.
Manage multiple concurrent AI projects across various business functions.
Establish project governance, reporting, risk management, and escalation processes.
Ensure projects are delivered on time, within scope, and aligned to business objectives.
Business Partnership & Strategy
Partner with business leaders to identify, evaluate, and prioritize AI use cases.
Translate business needs into actionable project roadmaps and technical requirements.
Develop business cases, value realization plans, and executive presentations.
Facilitate workshops to define opportunities, success metrics, and adoption strategies.
Cross-Functional Team Leadership
Coordinate activities across business teams, data scientists, AI engineers, technology teams, vendors, and external partners.
Drive collaboration among stakeholders with varying levels of AI maturity and technical expertise.
Remove delivery obstacles and proactively resolve issues impacting project success.
AI Governance & Risk Management
Ensure compliance with enterprise AI governance frameworks, model risk management standards, privacy requirements, and regulatory expectations.
Partner with Risk, Compliance, Legal, and Information Security teams throughout project lifecycles.
Monitor and mitigate delivery, operational, ethical, and model-related risks.
Change Management & Adoption
Develop stakeholder engagement, communications, training, and adoption plans.
Measure solution adoption, business outcomes, and user satisfaction.
Drive change management activities that support sustainable AI implementation.
Performance & Value Measurement
Define KPIs, success metrics, and benefits realization frameworks.
Track and report project progress, risks, budget performance, and value delivery.
Continuously identify opportunities to optimize AI solution effectiveness and ROI.
Required Qualifications
Bachelor's degree in Business, Technology, Data Science, Engineering, or related field.
5+ years of project or program management experience.
2+ years leading AI, advanced analytics, machine learning, automation, or digital transformation initiatives.
Experience managing cross-functional teams and complex enterprise programs.
Strong understanding of Agile, Waterfall, and hybrid delivery methodologies.
Experience developing executive-level presentations and business cases.
Proven ability to manage stakeholder relationships across business and technology functions.
Excellent communication, facilitation, and problem-solving skills.
Preferred Qualifications
Master's degree in Business Administration, Data Science, Computer Science, or related field.
PMP, PgMP, Agile, Scrum, SAFe, or equivalent certifications.
Experience with Generative AI platforms such as Microsoft Copilot, Azure AI, OpenAI, Anthropic Claude, or similar solutions.
Knowledge of machine learning lifecycle management (ML Ops) practices.
Experience within Financial Services, Insurance, Banking, Risk Management, or highly regulated industries.
Familiarity with AI governance, model risk management, data governance, and responsible AI principles.
Key Competencies
Leadership - Strategic thinking, executive presence, stakeholder influence, and decision-making.
Project Delivery - Program management, risk management, resource planning, and portfolio prioritization.
AI & Technology- Generative AI, machine learning concepts, data analytics, and intelligent automation.
Business Acumen - Process transformation, benefits realization, financial management, and operational excellence.
Change Leadership - Organizational change management, communications planning, training enablement, and user adoption