Job
Description
Key Responsibilities: End-to-End Project Delivery: Lead and manage the full lifecycle of AI/ML projects from initiation through deployment and maintenance. Team Leadership: Oversee cross-functional teams including data scientists, ML engineers, MLOps specialists, business analysts, and developers. Stakeholder Management: Interface with internal and external stakeholders to define requirements, scope projects, and communicate progress and risks. Project Governance & Methodology: Apply delivery best practices, including Agile, DevOps, and AI-specific workflows (e.g., model lifecycle management, responsible AI practices). Risk and Quality Management: Identify risks early and implement mitigation strategies while maintaining quality standards in model performance, scalability, and compliance. Technical Oversight: Provide technical guidance on AI architectures, data pipelines, model deployment, and monitoring. Work closely with solution architects and technical leads. Budget and Resource Planning: Manage project budgets, forecast resource needs, and optimize team utilization. Client Engagement & Growth: Support business development by contributing to proposals, solutioning, and roadmap planning with AI capabilities. Required Qualifications: 1015 years of experience in project/program management with at least 46 years focused on AI/ML or advanced analytics delivery. Strong understanding of AI/ML technologies, platforms (e.g., AWS/GCP/Azure ML, TensorFlow, PyTorch, etc.), and AI governance practices. Demonstrated ability to deliver AI solutions in production environments, preferably at scale in regulated or enterprise settings. Proven experience managing multi-disciplinary teams across geographies. Excellent communication, negotiation, and presentation skills. PMP, PRINCE2, or Agile certifications are a plus. Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. MBA is a plus. Preferred Experience: Exposure to domains such as finance, healthcare, manufacturing, or retail with real-world AI deployment experience. Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow, etc.). Familiarity with ethical AI principles, data privacy regulations (e.g., GDPR), and model risk management.
