AI Delivery Manager

About the Role

The AI Delivery Manager is a next-generation leadership role responsible for transforming how engineering teams deliver software using AI-powered systems, automation, and intelligent workflows. You’ll bridge Product and Engineering, design AI-enabled delivery frameworks, and ensure teams ship faster and more predictably. This role is central to building AI-powered delivery ecosystems — integrating MCP frameworks, AI agents, and automated governance into the software delivery lifecycle.

Your scope includes bridging product requirements with engineering execution, building AI-enabled delivery systems, conducting technical evaluation and architecture review, and owning delivery outcomes across programmes.

Responsibilities

• Bridge product requirements with engineering execution
• Translate product requirements into clear, executable engineering deliverables.
• Evaluate technical feasibility and architectural implications of product requests.
• Ensure requirements are testable, traceable, and automation-ready.
• Maintain traceability from requirements through development, testing, and deployment.
• Build AI-enabled delivery systems
• Design and deploy AI-powered delivery frameworks.
• Implement Model Context Protocol (MCP) based systems.
• Architect and orchestrate multi-agent AI workflows.
• Integrate AI governance, guardrails, and validation into CI/CD pipelines.
• Build reusable AI delivery accelerators and automation frameworks.
• Technical evaluation and architecture review
• Evaluate system architecture and engineering decisions across programmes.
• Assess AI frameworks, scalability risks, and vendor solutions.
• Review LLM implementations, vector databases, embeddings, and AI pipelines.
• Ensure governance, reliability, and security in AI systems.
• Delivery ownership and decision-making
• Own delivery outcomes across programmes and use AI tools, metrics, and data insights to make decisions.
• Identify delivery risks early, resolve dependencies proactively, and improve delivery predictability, lead time, and release quality.
• Program and stakeholder leadership
• Drive alignment between Product, Engineering, and Leadership, providing technical clarity to senior stakeholders.
• Maintain visibility into delivery health and programme performance; ensure release readiness and governance compliance.

Qualifications

• 4+ years of hands-on technical experience as a Software Engineer, Tech Lead, DevOps Engineer, Platform Engineer, ML Engineer, or Solutions Architect.
• Strong programming experience in Python, JavaScript, or similar languages, with hands-on experience designing and managing CI/CD pipelines.
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP, and a solid understanding of DevOps and MLOps practices.
• Experience building AI agent workflows and intelligent automation systems, with hands-on exposure to LangChain, AutoGen, CrewAI, or similar agent orchestration tools.
• Strong understanding of LLMs, embeddings, vector databases, RAG systems, and AI model evaluation and guardrails.
• Experience leading complex engineering or technical delivery initiatives, with strong command of Agile and flow-based delivery methodologies.
• Proven ability to drive delivery improvements using data, metrics, and automation across cross functional teams.
• Strong ability to evaluate technical solutions, architecture, and implementation approaches; excellent stakeholder communication and collaboration skills.
• Systems-thinking mindset with a focus on building AI-enabled delivery ecosystems.

Required Skills

• Hands-on experience implementing Model Context Protocol (MCP) systems in production.
• Track record of measurable improvements in lead time, release predictability, or escape-rate defects.
• Experience building and operating AI governance and guardrails inside CI/CD.
• Background bridging Product and Engineering org-design as well as delivery process.