Collaborates with cross-functional teams, including engineering, product, and business stakeholders, to define program scope, objectives, and deliverables, ensuring alignment with overall business goals
Utilizes advanced analytical reasoning to assess program performance, identify areas for improvement, and implement data-driven optimizations to enhance efficiency and effectiveness.
Engage, influence, and drive delivery of multiple GenAI initiatives with cross-functional, cross-product, and cross-line of business stakeholders.
Synthesize findings into clear, concise, customer-focused problem statements, recommendations, business cases, analysis, plans of action, and risk assessments
Drive organizational structure, efficiency, and improvement of operational practices.
Required qualifications, capabilities, and skills
5+ years of experience or equivalent expertise in technical program management, leading complex technology projects and programs in a large organization
Advanced expertise in stakeholder management, with the ability to establish productive working relationships and influence decision-making across cross-functional teams and clients
Advanced experience in utilizing technical fluency, including knowledge of vendor products and managing vendor relations, to enable on-demand access to shared pools of applications and services
Agile delivery mindset and usage of JIRA tool, SQL or JQL, and experience with CI/CD pipelines and software deployment tools.
Proven track record of effectively managing resources, budgets, and high-performing teams in a fast-paced, agile environment
Demonstrated proficiency in applying analytical reasoning and problem-solving techniques to break down business, technical, or operational objectives into manageable tasks and activities
Preferred qualifications, capabilities, and skills
Proven ability to navigate tight deadlines, organize and prioritize work, while effectively managing the dynamic landscape of GenAI.
Experience in organizational operation, Excel data analysis, and organization budgeting & planning
Experience or exposure in the AI ML, Data ecosystem space
Expertise on the AI and data product lifecycle, spanning from data discovery, data processing, model development, model deployment, and model monitoring
Microsoft (Azure AI Engineer Associate), Google (Professional Machine Learning Engineer), Amazon (AWS Certified AI Practitioner), AWS Cloud Practitioner Certification, or Microsoft Azure Fundamentals certifications, a plus
