Senior Technical Program Manager

Job description

Sprinklr is the definitive, AI-native platform for Unified Customer Experience Management (Unified-CXM), empowering brands to deliver extraordinary experiences at scale — across every customer touchpoint.  By combining human instinct with the speed and efficiency of AI, Sprinklr helps brands earn trust and loyalty through personalized, seamless, and efficient customer interactions. Sprinklr’s unified platform provides powerful solutions for every customer-facing team — spanning social media management, marketing, advertising, customer feedback, and omnichannel contact center management — enabling enterprises to unify data, break down silos, and act on real-time insights.  Today, 1,900&#43; enterprises and 60% of the Fortune 100 rely on Sprinklr to help them deliver consistent, trusted customer experiences worldwide.  Job Description 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.  <ul