#EG AI Delivery Project Manager

NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group.

Job Description

This role sits within NCS AI Central's (AIC) Forward Deployed Engineering (FDE) model — the combined capability that takes AI solutions from proof-of-concept through to hardened production systems. As AI Delivery Project Manager / Scrum Master, you own delivery of the FDE lifecycle for your squad (Discover → POC → POV → PRR → Scale → Operate), run Agile ceremonies tuned for AI/Gen AI's iterative, evidence-driven nature, and act as the primary link between engineering delivery, AI Mission, AI Experience, and client/business stakeholders — across both fast-moving FDE engagements and steady-state system development and maintenance work.

What Will You Do:

• FDE Lifecycle Ownership
• Own delivery of the squad's engagement through the FDE lifecycle — Discover, POC, POV, PRR, Scale, Operate — tracking where each workstream sits and what's needed to move it to the next gate.
• Plan and execute short-cycle POCs/POVs from concept to evaluation, including receiving and refining scoped opportunities handed off from AI Mission.
• Define minimal scope, success criteria, and timelines appropriate to each lifecycle stage; prioritize features based on value, feasibility, and constraints.
• Coordinate the PRR (Production Readiness Review) gate as the delivery-side owner — tracking the five-pillar checklist (Evaluation & Quality, Security/Governance/Compliance, Model & Token Strategy, and others) to readiness, in partnership with the AI Architect and AI/LLM Specialist.
• Agile Leadership for AI Squads (Scrum Master)
• Run lightweight Agile ceremonies — daily stand-ups, sprint planning & refinement, demo reviews, and retrospectives — adapted for AI/Gen AI work, where "done" often means a measured evaluation result, not just shipped code.
• Promote velocity, transparency, and continuous improvement across experimentation-heavy sprints.
• Unblock progress quickly — including chasing down data access, model access, or compliance sign-offs that commonly stall AI engagements — and minimize ceremony overhead.
• AI Delivery Risk & Feasibility Management
• Maintain a working risk register specific to AI delivery — hallucination/accuracy risk, data quality dependency, model/token cost exposure, and latency — and translate these into scope and timeline decisions the squad can act on.
• Advise stakeholders directly when a request is technically impractical, high-risk, or better served by a non-Gen AI approach, drawing on input from the AI Engineer, AI/LLM Specialist, and Data Scientist.
• Maintain working awareness of the Gen AI model landscape — including China-origin models (DeepSeek, Qwen, GLM) as increasingly viable, cost-effective options — sufficient to have an informed feasibility conversation with technical leads and clients.
• Own secure software delivery and governance practices as a standing checkpoint through the lifecycle, not just at PRR — flagging compliance gaps (IM8, PDPA, sector-specific) early enough to act on them.
• Stakeholder Alignment Across the FDE Model
• Act as the delivery-side connective tissue across AI Mission (opportunity handoff), AIIS (build/scale/govern), and AI Experience (design/adoption) for the squad's engagement.
• Translate problem statements into achievable POC/POV plans, and manage expectations around what can be delivered, when, why, and at what lifecycle stage.
• Facilitate clear communication between business and engineering, bringing a consulting mindset and customer-first attitude to stakeholder interactions.
• Socialize learnings, outcomes, and next steps; present outcomes and lifecycle-stage recommendations to stakeholders and decision makers up to Director level.
• Outcomes, Evaluation & Reporting
• Track POC/POV performance against defined success criteria, incorporating evaluation evidence (accuracy, hallucination rate, cost-per-query) from the AI/LLM Specialist rather than relying on qualitative impressions alone.
• Document findings, results, and lifecycle-stage recommendations (advance, iterate, or stop) clearly for both technical and business audiences.
• FDE & Development/Maintenance Coverage
• During FDE engagements: run lightweight, fast-cycle Agile ceremonies and POC/POV plans that keep pace with rapid client iteration, and drive the engagement toward a clean PRR gate decision.
• During system development & mai