• Mobilise each engagement . You convert the proposal and statement of work into a release plan, a prioritised backlog, milestones, acceptance criteria, a responsibility matrix and a governance cadence agreed with the client.
• Run agile delivery . You facilitate Scrum events where Scrum is used, and you manage workflow, work-in-progress limits and flow measures where Kanban is used. Where a client needs fixed-fee milestones or formal stage gates, you adapt governance to those contractual and regulatory requirements.
• Lead the delivery team . You agree capacity and commitments with the engineers, remove blockers and set working norms for teams of typically three to ten engineers. The team often spans CFGI engineers and partner engineers in several Asia-Pacific time zones. You coach junior team members, while the engineers' line management stays with the partner.

Own the client relationship day to day . You prepare and chair steering committees, write clear status reports and manage expectations. You raise risks and decisions early, in business terms, so that sponsors can act before an issue affects the timeline.

• Control scope, budget and commercials . You track effort against fixed-fee and time-and-materials budgets, forecast the cost at completion and protect the engagement margin. You include cloud, platform and model-consumption costs in the forecast. You also separate backlog refinement within the agreed scope from changes to contractual scope, fees or milestones, which need the client's signed approval before work starts.
• Manage risks, dependencies and quality . You keep the risks, assumptions, issues and dependencies log current. Typical dependencies include client data access, environment provisioning and security approvals. Your definition of done covers testing, evaluation thresholds, documentation and the control evidence that regulated clients

require.

• Steer artificial-intelligence delivery specifically . You agree stop, pivot and scale criteria with the sponsor before a proof of concept starts. A solution moves to production only when evaluation results meet the agreed threshold and the model-risk and data-privacy sign-offs are in place. You also design human review steps with the client's process owners.
• Coordinate release readiness . With the architect and the client's owners, you confirm security and privacy review, monitoring, rollback and support ownership before each release. For agentic solutions, you agree the permitted actions, human approvals and escalation paths.
• Plan validation in regulated work . In healthcare and life-sciences engagements where good-practice (GxP) rules apply, you plan validation and its evidence from the start. You also bring clinical, quality and safety owners into the design of acceptance tests.
• Hand over to production and to run . You plan cut-over, hypercare and the transition to the client's team or to a CFGI managed service, with runbooks, service levels and support arrangements agreed in advance. The handover names who owns model performance in production, including the drift thresholds and the run-cost budget.
• Build the practice's delivery playbook . You create reusable plans, templates and estimation baselines from each engagement. You also support the partner on proposals by preparing estimates, staffing plans and delivery approaches.

• 6+ years of experience in technology delivery, with at least 3 years leading client-facing data, analytics, artificial-intelligence or software-engineering projects.

• Hands-on experience leading engineering teams of three or more, including distributed teams and teams combining internal, vendor or partner engineers.
• Practical experience running Scrum and Kanban teams and selecting agile, waterfall or hybrid delivery approaches to suit client needs.
• Experience translating statements of work into delivery plans, backlogs, milestones, acceptance criteria and governance routines.
• Experience managing fixed-fee and time-and-materials budgets, forecasts, change requests, subcontractor delivery and engagement economics.
• Technical fluency across data platforms and pipelines, APIs, system integration, cloud environments and continuous integration and delivery.
• Understanding of AI delivery, including retrieval-augmented generation, agents, model evaluation, machine-learning operations, data readiness and production monitoring.
• Experience working with software development lifecycle practices, testing, change management, security reviews and release governance.
• Experience communicating with clients and executives, including chairing steering committees and explaining technical status, risks and trade-offs in business terms.
• Working awareness of data-protection obligations, including Singapore’s Personal Data Protection Act and relevant laws in other client jurisdictions.
• Experience applying practical AI governance, including relevant Singapore Model AI Governance Frameworks, alongside applicable law and client policy.

• Experience with delivery tools such as Jira, Azure DevOps or Confluence.
• Relevant agile or project-management certifications, such as Professional Scrum Master, Certified ScrumMaster, PMI Agile Certified Practitioner, SAFe Scrum Master, Project Management Professional (PMP) or PRINCE2 Practitioner.
• Familiarity with delivery and governance approaches such as the V-model, PRINCE2, the PMI PMBOK Guide, SAFe and DevOps practices.
• Understanding of data and lakehouse platforms such as Databricks, Snowflake or Microsoft Fabric, and cloud platforms such as AWS, Microsoft Azure or Google Cloud.
• Healthcare and life-sciences delivery experience, including GxP environments, computerised system validation, or clinical and commercial data projects.
• Experience delivering finance-function or enterprise-resource-planning programmes.
• Knowledge of cyber security and IT governance frameworks such as ISO/IEC 27001, the NIST Cybersecurity Framework or COBIT.
• Experience with IT general controls and finance controls, including access, change management, IT operations, approvals, reconciliations and segregation of duties; SOX Section 404 experience is also useful.
• Experience transitioning solutions into managed services, including service-level design and IT service-management practices such as ITIL.

• A delivery leader who stays close to engineering and can challenge estimates, read architectures and assess technical trade-offs with architects.
• Someone who is comfortable managing uncertainty in AI projects through time-boxed experiments, evaluation-based acceptance criteria and early data-readiness checks.
• A clear, pragmatic communicator who builds trust with clients, engineers, partners and executive sponsors.
• A structured problem-solver who balances delivery quality, commercial discipline, security, privacy and client outcomes.

This role offers a rare combination of hands-on delivery leadership and a genuine commercial mandate within a fast-growing Data & AI practice, backed by a global firm with an established Data & Analytics capability and AI-enabled managed-services platform.

You will have exposure to the full commercial and delivery lifecycle — from the first executive conversation, through opportunity qualification and solution design, to signed engagement, governed implementation, and long-term client expansion.

The work spans sectors and the full Data & AI lifecycle, from strategy and readiness through architecture, governed build, automation, analytics, artificial intelligence, and managed operation.

CFGI’s entrepreneurial model gives Managers meaningful client ownership, visibility with senior leadership, and the opportunity to help shape the growth of the Data & AI practice across APAC. As the practice scales, professionals who combine strong delivery, technical credibility, client relationships, and business-development success will be well positioned for continued progression within the firm.