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.

Solid 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.

📌 AI Delivery Manager (Hyderabad)
🏢 ProductSquads
📍 Hyderabad