Agentic Engineering Manager
Around 12-18 years in Engineering Management, Program Management, AI Transformation, or Digital Engineering Lead end-to-end delivery of Agentic AI programs across multiple business domains. Manage program scope, timelines, budgets, risks, dependencies, and stakeholder expectations. Establish scalable delivery frameworks for AI-native and autonomous workflows. Drive Agile / SAFe delivery practices for AI transformation initiatives. Ensure successful deployment, adoption, and continuous optimization of AI agents. Bachelor’s or Master’s degree in Computer Science, Engineering, Business, or related field. 12-18 years of experience in IT Delivery, Digital Transformation, or Program Management. 3 years of experience in AI/ML, Intelligent Automation, or Generative AI programs. Strong understanding of: Agile Methodologies (SAFE, Spotify) DevOps Generative AI LLM ecosystems Agentic workflows AI orchestration frameworks Enterprise AI platforms Experience delivering cloud-native solutions on Azure, AWS, or GCP. Strong stakeholder management and executive communication skills. Experience leading globally distributed teams. Experience with: Microsoft Copilot Studio Azure AI Services OpenAI / Azure OpenAI LangChain / Semantic Kernel Power Platform AI observability tools Knowledge of: RAG architectures Multi-agent frameworks AI governance models Prompt engineering MLOps / LLMOps Certifications preferred: SAFe Agilist Agentic AI certification (Claude or equivalent) Microsoft AI certifications Azure Solutions Architect Strategic Thinking AI Delivery Leadership Stakeholder Management Innovation Mindset Risk & Governance Management Enterprise Transformation Data-Driven Decision Making Cross-functional Collaboration Change Leadership Collaborate with AI architects, data scientists, prompt engineers, and platform teams. Ensure responsible AI, governance, explainability, transparency, and compliance standards. Provide executive-level program reporting,value realization metrics, and strategic recommendations. Establish AI governance frameworks including: Ensure alignment with enterprise architecture and compliance standards. Build reusable delivery of accelerators, templates, and AI operational playbooks. Improved business productivity and user adoption Delivery predictability and governance compliance