AI Delivery Manager

ProductSquads; Hyderabad; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

ProductSquads in Hyderabad is seeking an AI Delivery Manager to lead AI-powered software delivery initiatives. You will bridge product and engineering, design delivery frameworks, and drive faster, more predictable releases using MCP, AI agents, and automated governance.

You will own delivery across programmes, build AI-enabled workflows, evaluate architectures, and ensure governance and security. The role demands 4+ years in software engineering/leadership, cloud experience, and strong

Qualifications

• 4+ years in software engineering, tech lead, or similar delivery roles.
• Experience with cloud platforms (AWS/Azure/GCP) and MLOps.
• Proven ability to lead AI agent workflows and automation.
• Strong understanding of LLMs, embeddings, and vector stores.
• Experience driving delivery improvements with data and automation.

Responsibilities

• Bridge Product and Engineering to define deliverables.
• Design AI-enabled delivery frameworks and MCP-based systems.
• Evaluate architecture and assess AI networks and guardrails.
• Ensure requirements are testable, traceable, and automation-ready.
• Own delivery outcomes across programmes and monitor health.
• Lead cross-functional teams and align stakeholders on roadmap.

Skills

AI delivery

Cloud platforms (AWS/Azure/GCP)

DevOps/MLOps

LangChain

AutoGen

CrewAI

LLMs

Embeddings

Vector databases

Agile delivery

Data-driven decisions

Stakeholder communication

AI governance

Tools

LangChain

AutoGen

CrewAI

Job description

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.
• 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.