Project Planning & Execution:
Define project scope, objectives, deliverables, and timelines for AI/ML initiatives.
Develop detailed project roadmaps, milestones, and risk assessments to ensure timely execution.
Work closely with engineering, data science, and product teams to align on project goals.
Stakeholder & Cross-Team Collaboration:
Act as the main point of contact between technical teams, business stakeholders, and leadership.
Translate technical AI/ML concepts into clear business objectives and actionable insights.
Facilitate regular status updates, sprint planning, and retrospectives with Agile methodologies.
Risk & Performance Management:
Proactively identify and mitigate project risks, dependencies, and blockers.
Track KPIs, performance metrics, and AI model success rates to ensure continuous improvement.
Manage project budgets, timelines, and resource allocation to optimize efficiency.
AI & Technology Integration:
Oversee AI model deployment processes, cloud integration, and system scalability.
Ensure that LLM-based applications, RAG pipelines, and AI solutions align with business needs.
Work with DevOps teams to ensure CI/CD pipelines, security, and compliance are properly implemented.