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.
- Robust 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.

📌 AI Delivery Manager (Hyderabad)
🏢 Spore N Sprouts
📍 Hyderabad