Technical Project Manager

Experience : 15+ Year

Role Purpose

Own the day-to-day delivery and execution of Agentic AI initiatives, ensuring sprint-level progress, quality delivery, and successful deployment of AI solutions into production.

Key Responsibilities

Delivery Execution and Sprint Management

• Manage end-to-end delivery at sprint and release level

• Plan and track sprint backlogs, user stories, and deliverables

• Ensure adherence to timelines, scope, and quality standards

• Monitor team velocity, burndown, and delivery KPIs

Team and Workstream Coordination

• Coordinate across Engineering, development, and architecture teams

• Drive daily stand-ups, sprint planning, reviews, and retrospectives

• Identify and resolve blockers impacting delivery

• Ensure clear task ownership and accountability

Agentic AI Implementation Oversight

• Track development of multi-agent workflows (planner, executor, memory, etc.)

• Ensure delivery of components such as orchestration, APIs, and integrations

• Support testing cycles including functional, integration, and UAT

• Ensure readiness for deployment and release

LLMOps and Release Management

• Ensure implementation of:

o Prompt and model versioning

o Testing and evaluation pipelines

• Support release management across environments (dev/test/prod)

• Track operational metrics such as latency, defect rates, and token usage

Quality and Risk Management

• Ensure adherence to testing standards and acceptance criteria

• Track and mitigate delivery risks and issues

• Ensure compliance with security and governance requirements

Stakeholder Communication

• Provide regular updates on sprint progress and delivery status

• Escalate risks, delays, and dependencies proactively

• Coordinate with business teams during UAT and rollout

Technical Stack

Must Have

• Experience in Agile delivery (Scrum/Kanban) for technology programs

• Exposure to AI/ML or Generative AI project delivery

• Understanding of:

o Agentic AI workflows and LLM-based applications

o API integrations and distributed systems

• Experience with cloud environments (AWS/ GCP)

• Strong delivery tracking, reporting, and coordination skills

Good to Have

• Exposure to LangChain, LangGraph, CrewAI, or AutoGen

• Familiarity with LLMOps concepts and tools

• Understanding of RAG pipelines or vector databases

• Experience with Jira, Confluence, or similar tools