Experience: 4-6 years in technical program management, including at least 3 years on engineering-adjacent or AI/ML programs  

Team: PMO, within the CTO organisation 

Job Location: Gurgaon (On-site)  
 

About this Role

You will run large, cross-functional technical programs end to end. You will also help change how this PMO works — building agent-assisted workflows that replace manual reporting, coordination, and governance effort for the entire team, not just for yourself. 

We are not looking for someone who has used an AI assistant to draft status updates. We are looking for someone who has built something with these tools that other people can use. 
 
What You’ll Do  - PROGRAM DELIVERY 

• Drive multiple large-scale technical programs, working cross-functionally with Product Management, Design, Software Engineering, Business Operations, and external partners to guarantee smooth and efficient delivery. 

• Serve as the single point of contact for delivery across the SDLC, bringing the right balance of technical depth, program management rigour, and product judgement. 

• Track complex programs and communicate status, risk, and trade-offs clearly to technical teams, non-technical stakeholders, and senior leadership. 

• Define and implement cross-team processes that improve delivery efficiency — and define the metrics that prove whether they worked, driving adoption across organisations. 

• Manage stakeholders across functions by setting expectations, holding them, and providing frequent, credible program updates. 

• Handle multiple competing and shifting priorities in a fast-paced environment. 

• Partner with Data Science, Engineering, and Product to deliver scalable AI-powered solutions — aligning on data readiness, model performance criteria, and deployment timelines. 

• Lead AI/ML initiatives across the full lifecycle, from problem definition through model development, deployment, and monitoring. 

• Bring MLOps discipline to AI program delivery, including governance around model performance, risk, bias, and compliance. 

How you'll work — AGENTIC DELIVERY OPERATIONS 

• Build shared context, not personal prompts. Author and maintain the PMO's shared context assets — portfolio structure, governance conventions, reporting standards, escalation paths — so that agent output is consistent across the team rather than dependent on who is asking. 

• Turn recurring PMO work into reusable, versioned capability. Package repeated workflows — status rollups, RAID reviews, steering-committee packs, dependency analysis — as shared skills and agents that the whole PMO uses and improves, maintained in Git like any other engineering asset. 

• Connect agents to systems of record. Work with Platform and Security to wire agent access to Jira, self-hosted GitLab, SharePoint/OneDrive, and Office — and define where write access is and is not appropriate. 

• Automate the reporting cadence, then retire the manual version. Stand up recurring, unattended analyses that deliver into the PMO's channels. Success here is measured by manual processes decommissioned, not by tools adopted. 

• Own quality and judgement on agent output. Design the human-in-the-loop checkpoints: what must be reviewed before it reaches an executive, how a confidently wrong rollup gets caught, and where automation should be declined outright. 

• Raise the team's ceiling, not just your own. Coach other program managers, review their contributions to the shared library, and hold the quality bar as it grows. 

What makes you qualified?  

 
Must Have:

• 4+ years in technical program management or TPM/PMO leadership on engineering-heavy programs, with demonstrable ownership of portfolio-level delivery — not single-project coordination. 

• Evidence you have built something with AI tooling that other people used — an automation, a shared workflow, a reusable template or agent. Personal productivity gains alone do not meet this bar. Be ready to walk us through what you built, who used it, and what it replaced. 

• Working fluency with agentic AI tooling (Claude, Claude Code, Microsoft Copilot, or equivalent): prompt and context engineering, tool and connector configuration, and a realistic understanding of where these systems fail. 

• Comfort with Git and markdown-based workflows. You do not need to write production code. You do need to version a file, open a pull request, and read a script well enough to know what it does. 

• Technical literacy sufficient to hold your own with senior engineers on architecture, dependencies, and delivery trade-offs. 

• Executive-grade written communication — structured, concise, decision-oriented. 

• Hands-on experience with Jira and Confluence; familiarity with GitLab or GitHub workflows. 

• You have replaced a recurring manual reporting process with an automated one, and can quantify the outcome. 

• Experience with MCP servers, connector configuration, or scripting (Python or equivalent) for data pulls and light ETL. 

• Delivery experience on AI/ML programs across the full lifecycle — problem framing through deployment and monitoring. 

• Experience introducing a new way of working to a sceptical team, with evidence it stuck after you stopped pushing. 

• Exposure to AI governance frameworks — model risk, bias, or compliance. 

• Enterprise SaaS or CXM domain background. 

• PMP, SAFe, or equivalent certification — useful, not required. Demonstrated judgement outranks certification for this role.