Technical Manager/ AI Delivery Manager

We are looking for a hands-on Technical Manager to take end-to-end ownership of client deliveries. From kickoff through to go-live and handover to BAU, you will own the plan, lead the engineering pod, and be the primary client contact on delivery.

This is not a pure coordinator role. You will need real technical depth - to question an estimate, debate an architecture, and guide trade-off decisions with architects and engineers.

Location:
Singapore - hybrid working with regular presence at client sites. Some travel within APAC may be required.

What You Will Do

Shape the engagement at the start:
Turn proposals and SOWs into a practical delivery setup - release roadmap, backlog, milestones, success criteria, RACI and governance that the client actually signs up to.

Run delivery, day-to-day:
Lead teams working in Scrum and Kanban. Facilitate planning, stand-ups and retros, manage flow and WIP, and adapt the approach where clients need fixed-price gates or formal approvals.

Lead the team:
Manage a pod of 3-10 engineers, often split across our client and partner teams in different time zones. Set norms, unblock the team, manage capacity, and mentor junior members. No direct line management.

Own client delivery governance:
Run SteerCos, write sharp status updates that executives can read in 5 minutes, and flag risks, dependencies and decisions early in business terms.

Own numbers and scope:
Track time and spend against fixed-fee and T&M budgets, forecast cost-to-complete including cloud and model costs, protect margin, and manage formal change control.

Own risk and quality:
Keep RAID live. Ensure Definition of Done covers testing, model evaluations, docs, and the evidence regulators need. Handle typical blockers like data access, infra, and security approvals.

Lead AI delivery with rigour:
Set clear rules before any PoC - when to stop, pivot, or scale. Only move to production when evaluation scores are met and privacy and model-risk approvals are in place. Design human-review steps with business owners.

Get it live and keep it live:
Ensure security reviews, privacy checks, monitoring, rollback plans and support ownership are done before release. For agents, lock down what they can do and when they need human approval. Plan cutover, hypercare, and handover to the client's run team or to a managed service - including runbooks, SLAs, who owns model performance, drift thresholds and run cost.

Improve how we deliver:
Build reusable playbooks, templates and estimation benchmarks from your work. Help shape proposals with delivery approach, staffing and estimates.

What You Bring

We are hiring for delivery craft, not keywords. The ideal profile has been close to engineering before - ex-engineer, tech lead, or technical BA.

6+ years in tech delivery, with at least 3 years leading data, analytics, AI or software engineering work for external clients. Consulting / SI experience is strongly preferred.

Proven hands-on use of Scrum and Kanban. You know Jira / Azure DevOps / Confluence well. Certifications like Scrum.org / Scrum Alliance / PMI-ACP / SAFe are a bonus, not a must.

You understand SDLC, can work in agile, waterfall or hybrid, and know how validation and evidence work under any model. Familiar with governance models like PRINCE2 / PMBOK.

You are fluent across the stack: lakehouse - Databricks / Snowflake / Fabric, pipelines, APIs, integrations, cloud - AWS / Azure / GCP, and CI/CD.

You understand what makes AI delivery different: RAG, agents, evals, and MLOps/LLMOps, and why we need data-readiness checks, eval-based acceptance and prod monitoring.

You have led pods of 3+ engineers, including distributed teams and mixed vendor teams.

You have managed budgets, forecasts, scope changes and subcontractors. PMP / PRINCE2 Practitioner is a plus.

You communicate clearly with senior stakeholders and can explain technical issues in business language.

You have worked inside client change, test and security processes. You have working knowledge of data privacy - e.g., Singapore PDPA - and practical AI governance - e.g., Singapore Model AI Governance Frameworks for Generative AI and Agentic AI.

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