Loại hình
Toàn thời gian
Hình thức
Tại văn phòng
Cấp bậc
Quản lý / Giám sát
Ngành nghề
Ngành nghề khác
Phòng ban
VinSmart Future
Mức lương
Thương lượng
Địa điểm
Thành phố Hồ Chí Minh, Hồ Chí Minh
Tổng quan
- Project planning & feasibility
- Develop realistic delivery plans for AI products and programmes, balancing product ambition with technical feasibility, data readiness, team capacity, and organisational constraints. Assess feasibility before committing to timelines, including model development, experimentation, integration, deployment, and production readiness. Build schedules that account for uncertainty in both product…
Yêu cầu
- Treat evolving requirements, model performance uncertainty, data limitations, and changing business rules as core delivery constraints. Maintain a live risk register and map dependencies across product, engineering, data, ML, operations, and business teams. Build contingency into plans at the points of highest uncertainty. Distinguish between risks that can be mitigated through delivery decisions…
- Cross-functional AI product delivery
- Lead delivery across product, engineering, data science/ML, data engineering, operations, and business teams simultaneously. Translate business objectives into clear delivery outcomes while ensuring technical and model-related constraints are understood by stakeholders. Remove blockers before they surface in stand-ups and maintain momentum across workstreams even when experimentation or…
- Stakeholder alignment & governance
- Drive alignment across competing product, commercial, technical, and operational priorities without defaulting to consensus. Establish governance structures proportional to programme complexity, including change control, issue resolution, escalation paths, AI/model review, and executive reporting. Make decisions visible and traceable, particularly when scope, model performance, data requirements,…
- Outcome accountability beyond go-live
- Define what success looks like in measurable business and product terms before delivery begins. Establish appropriate measures across adoption, operational impact, model/product performance, reliability, and business outcomes. Track these metrics through launch and beyond. Drive post-delivery reviews that identify improvements to the AI product, delivery approach, and operating model — not simply…
- Planning depth & feasibility judgment
- Ability to construct and challenge delivery plans at the level of tasks, dependencies, team capacity, data readiness, technical constraints, experimentation cycles, and organisational readiness — not just milestones. Track record of identifying infeasible commitments and surfacing delivery constraints before they become programme failures.
- Proven experience managing programmes where product requirements, business rules, data availability, model performance, or technical constraints changed significantly during execution. Comfortable working with iterative experimentation and translating learning into practical decisions on scope, sequencing, and timelines without losing delivery coherence or stakeholder confidence.
- Technical AI / programme experience
- Demonstrated experience delivering AI/ML-enabled products or intelligent automation programmes, including one or more of:
- ML model deployment and productionisation
- AI feature integration into existing products or platforms
- Intelligent automation at enterprise scale
- Data platform or data-intensive product delivery
- AI-enabled enterprise system transformation
- Enough technical fluency to engage credibly with engineers, data scientists, ML engineers, and architects, ask the right questions, identify delivery dependencies, and challenge unrealistic assumptions — without needing to be the hands-on technical owner.
- AI product lifecycle understanding
- Experience working across the AI product lifecycle, including experimentation, data preparation, model development, evaluation, deployment, monitoring, iteration, and lifecycle management. Understand that AI outputs can be probabilistic and that model performance, data quality, and production behaviour may evolve after launch. Able to set realistic expectations with business stakeholders around…
- Multi-stakeholder complexity
- Track record navigating organisations where delivery authority is distributed across product, engineering, data/ML, business functions, sponsors, and external partners. Influence without authority is the default operating mode, not the exception.
- Innovative problem-solving under pressure
- Demonstrated ability to identify non-obvious delivery paths when conventional approaches are blocked — including restructuring scope, resequencing dependencies, changing the experimentation approach, using alternative technical solutions, or adjusting the product rollout strategy. Escalation comes with options and clear trade-offs, not escalation alone.
- Resilience & sustained execution
- Evidence of maintaining delivery quality and team morale through sustained pressure, including changing business priorities, model or data limitations, budget changes, sponsor turnover, organisational restructuring, or repeated scope revision. Composure under pressure is observable, not claimed.
- Governance without bureaucracy
- Ability to design governance structures that protect AI product delivery without slowing it down. Understands when additional process, review, or controls add value — particularly around data, model risk, production readiness, and business impact — and when they simply add friction.
- Minimum 8 years of experience in project or programme management, with increasing delivery scope and complexity.
- Proven experience delivering non-greenfield, integration-heavy, or enterprise-scale programmes.
- Demonstrated experience delivering AI/ML-enabled products, intelligent automation, or programmes involving ML model deployment and productionisation.
- Experience with AI product development cycles, experimentation, model lifecycle constraints, data dependencies, and probabilistic outputs.
- Experience delivering ERP, CRM, core operational systems, or other enterprise platforms involving complex data migration, legacy constraints, integrations, and multi-team dependencies.
- Strong technical fluency across software engineering, data, cloud/platform, and AI/ML concepts sufficient to lead technical delivery discussions.
- Bachelor’s degree in Engineering, Information Systems, Computer Science, or equivalent.
- PMP, PRINCE2, or equivalent project/programme management certification is an advantage.
Quyền lợi
- Flexible working hours and attendance policy (Work from Home on working Saturdays).
- Attractive compensation and bonus packages, highly competitive in the market.
- Exclusive employee benefits across the Group’s ecosystem in accordance with company policies.
- Opportunity to work on large-scale and strategic technology projects.
- Professional technology environment with leading scientists, experts, and engineers from top technology companies in Vietnam and around the world.
- Free access to learning platforms such as Udemy, Coursera, and O’Reilly; internal workshops; sponsorship for professional certifications; and exclusive mentoring programs from the Group and Company leadership team.
- Full statutory insurance coverage in accordance with Vietnamese Labor Law (Social Insurance, Health Insurance, Unemployment Insurance), along with private healthcare insurance based on job grade and annual health check-ups at reputable hospitals and healthcare centers nationwide.
- Participation in internal activities, team-building programs, and annual company events.
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