Senior Manager, AI Engineering

MOH Holdings (Singapore); Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

MOH Holdings (Singapore) is seeking a hands-on Senior Manager, AI Engineering to translate business challenges into production-ready AI and data products. You will design end-to-end AI/data solutions, build scalable pipelines and establish reusable engineering foundations to enable measurable business impact.

You will collaborate with stakeholders, technology teams and external partners to deliver production-grade AI solutions, ensure governance and security, and mentor colleagues to evolve

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science or related technical discipline.
• Strong software engineering fundamentals with hands-on Python, SQL and production stack experience.
• Experience designing and building APIs, data pipelines and enterprise integrations.
• Experience with AI patterns including model APIs and retrieval-based approaches.
• Cloud platform experience and modern DevOps practices (CI/CD, IaC).
• Knowledge of data governance, privacy and responsible AI considerations.

Responsibilities

• Shape solution and architecture by translating business problems into AI/data product designs.
• Build and deploy production-grade AI applications and data pipelines.
• Establish reusable engineering foundations and governance for AI solutions.
• Provide technical leadership, code reviews and mentorship to teams.
• Ensure security, reliability and maintainability of AI systems.

Skills

Python

SQL

APIs

Data pipelines

Cloud platforms

Architectural design

Code review

Hands-on engineering

Problem solving

Education

Bachelor's degree in Computer Science / related technical discipline

Tools

Python tooling

APIs & integration tools

Job description

As the holding company of Singapore’s public healthcare institutions, we are always looking for qualified, passionate individuals who are keen to make a valuable contribution to public healthcare. At MOH Holdings, we believe our employees are our greatest asset and we are dedicated in helping them achieve their full potential through professional development and by providing an environment to develop their leadership skills and competencies.

The AI and Data Office (AIDO) drives MOHH's enterprise AI and data agenda through its Strategy & Governance and Architecture & Engineering teams. While the Strategy & Governance team focuses on strategy, governance and adoption, the Architecture & Engineering team designs, builds and operates AI and data products that solve business problems, improve decision-making and strengthen corporate and shared-service operations. As a member of the Architecture & Engineering team, you will help bring AI solutions from concept to production and deliver measurable business impact.

Join us and be part of a team - a uniquely MOHH spirit that propels us forward through every circumstance we face.

• Acting Tomorrow For Today: We work pragmatically with the realities of the present, with a mindset that is geared to the needs of the future.
• Starting Where You Can: We take a can-do approach to problem-solving, even if it means starting small, because every contribution counts.
• Leaving No Job Undone: We take pride in seeing things through. Our high standards means it’s not just about getting the job done, but getting it done well.
• Moving Together as One: Care for all starts within, with us as an organisation. We look out for one another, leaving no on behind as we grow.

SUMMARY OF THE ROLE

We are looking for a hands-on Senior Manager, AI Engineering to help translate business challenges into production-ready AI and data products. This is a senior technical individual contributor role for someone who enjoys designing systems, writing and reviewing code, solving complex engineering problems and bringing AI solutions from concept to production.

You will work closely with business stakeholders, technology teams and external partners to shape solutions, build production-grade AI products and establish reusable engineering foundations that enable AI to create measurable business impact.

JOB RESPONSIBILITIES

Shape Solution and Architecture

• Work with business stakeholders to understand problems, users, workflows and intended outcomes before selecting technology.
• Design end-to-end AI and data solutions that combine applications, models, data, integrations and cloud services to solve real business problems.
• Make pragmatic recommendations on whether to build, buy, compose, reuse or enhance solutions, balancing value, delivery speed, risk and long-term sustainability.
• Use experimentation and prototyping to test ideas early, reduce uncertainty and inform implementation decisions.
• Define architecture patterns and engineering standards that enable solutions to be operated, maintained and scaled effectively.

Build and Operate AI Products

• Design, build and deploy production-grade AI applications, including generative AI, retrieval-based solutions and agentic workflows where appropriate.
• Build or integrate the software required to make AI useful in real business workflows, including applications, APIs, data pipelines and enterprise integrations.
• Select and apply AI technologies based on business needs, operational constraints and measurable outcomes rather than novelty.
• Integrate AI solutions with enterprise systems and trusted data sources while maintaining appropriate governance and controls.
• Design solutions that can operate safely and reliably, including graceful failure, fallback mechanisms and human review where required.

Build Shared Data, Cloud and Engineering Foundations

• Define data architecture patterns that support scalable, secure and maintainable AI and data products.
• Build reusable cloud foundations across development, test and production environments to support secure, reliable and scalable AI products.
• Establish shared engineering capabilities such as deployment pipelines, infrastructure as code, automated testing and reusable service templates.
• Establish pathways that enable successful prototypes to transition into production services without unnecessary rebuilding or unmanaged workarounds.
• Work closely with technology teams, platform owners and vendors to establish scalable and sustainable engineering practices.

Engineer for Evaluation, Security and Reliability

• Establish evaluation, testing and release practices that enable AI products to be deployed confidently into production.
• Build appropriate controls across data, systems, models and third-party services.
• Monitor product quality, reliability, performance and operational outcomes, and continuously improve them over time.
• Ensure products are supportable, resilient and operationally sustainable.

Technical Leadership & Capability Development

• Provide technical leadership across assigned products and initiatives, maintaining a coherent technical design from concept to production.
• Review architectures, code and vendor deliverables, challenging unnecessary complexity and promoting maintainable solutions.
• Communicate technical risks, trade-offs and recommendations clearly to stakeholders.
• Mentor colleagues and help successful experiments evolve into sustainable production services and reusable engineering patterns.

JOB REQUIREMENTS

Education Requirement(s):

• Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science or a related technical discipline, or equivalent evidence of engineering capability.

Technical Capabilities:

• Strong software-engineering fundamentals and hands‑on proficiency in Python, SQL and at least one production application or service‑development stack.
• Experience designing and building APIs, data or document pipelines, asynchronous workflows, persistent storage and integrations with enterprise systems.
• Practical experience with modern AI application patterns, including model APIs, retrieval‑augmented generation, tool use or agents, evaluation, guardrails and human‑in‑the‑loop workflows.
• Ability to diagnose whether performance issues originate in the model, source data, retrieval, application code, integration or surrounding business workflow, and to test each component separately.
• Experience with a major cloud platform and production practices such as containers, infrastructure as code, continuous integration and deployment, automated testing, observability, identity and access management, and secrets handling.
• Sound understanding of cybersecurity, privacy, data governance and responsible AI considerations across the system lifecycle.
• Ability to critically evaluate vendor solutions and technical proposals.

Key Qualities:

• Strong ownership and accountability, with the ability to follow a solution from problem definition through production operation and continuous improvement.
• Practical judgement in selecting the simplest fit‑for‑purpose approach and balancing innovation, delivery speed, cost, risk and maintainability.
• Clear communication, with the ability to explain architecture, evidence, risks and trade‑offs to technical and non‑technical stakeholders, including senior management.
• Constructive challenge and collaboration across business, technology, governance and vendor teams.
• Disciplined learning, using tests, operational data, incidents and user behaviour to improve both products and engineering practices.

Years of Experience Required:

• At least 8 years of relevant experience in software engineering, data engineering, machine‑learning engineering, platform engineering or a related technology discipline, including substantial responsibility for production systems.
• Experience taking AI, machine learning or data‑intensive products from prototype to production.
• Experience providing technical leadership through architecture decisions, code reviews, engineering standards, mentoring or vendor oversight.
• Exceptional candidates with fewer years of relevant experience but equivalent technical depth and impact may be considered

Advantageous Experience:

• Building AI products in healthcare, government, financial services or another regulated environment.
• Delivering document‑intelligence, knowledge‑retrieval, workflow‑automation, conversational‑AI or agentic‑AI products.
• Working across cloud, managed‑platform and enterprise‑software environments.
• Designing systems that process sensitive corporate, workforce, financial or operational data.
• Managing technical suppliers or working in an owner‑operator model where platform operations and business risk accountability sit with different parties.