Senior AI Solutions Engineer

ComfortDelGro is a leading multi-modal transport operator offering a comprehensive suite of transportation solutions. With operations across 13 countries, we provide public transport including buses and rail, point-to-point transport with taxis and private hire cars as well as business-to-business mobility solutions, providing safe and reliable journeys for millions daily. Guided by our purpose, ‘Mobility for a better future’, we are committed to driving positive impact and shaping a sustainable future in mobility for all our stakeholders.

We’re a purpose-driven, values-led organisation committed to delivering positive impact for a better future. Our Employees are united by The CDG Way – Collaboration, Drive and Growth. It is a culture defined by teamwork, shared goals, and a commitment to growing together. Whether you’re part of our bus, taxi, and rail teams, you’ll be a part of a global family working to drive positive impact through innovative solutions and helping to build a more resilient and sustainable organisation.

Join us, and be part of something bigger as we embark on a fulfilling journey towards your career.

Job Responsibilities & Duties

Ai Solution Development and Engineering

• Design, prototype and deliver advanced AI systems across GenAI, ML, AI agents and agentic architectures from concept and POC through production and international rollout.
• Develop production‑grade pipelines for data ingestion, feature engineering, training, evaluation, optimisation and deployment with full MLOps (CI/CD, versioning, monitoring, drift detection, rollback).
• Develop, fine‑tune and integrate ML and LLM models into business applications; including RAG pipelines, vector search and prompt‑engineering strategies.
• Create reusable, cloud‑agnostic components, APIs and microservices for consistent global deployment while driving continuous improvement.
• Design and optimise AI models and pipelines for performance, latency, cost and scalability applying FinOps principles with guardrails and quotas to ensure predictable and efficient cloud spend

Architecture and Integration

• Translate business requirements into scalable, enterprise‑grade AI architectures and technical designs.
• Integrate AI capabilities into platforms, workflows, applications and automation frameworks.
• Ensure all solutions meet stringent standards for performance, security, compliance, observability and reliability.
• Architect cloud agnostic AI systems across Azure, AWS, GCP and VMware using containerisation and modern deployment patterns.
• Define and enforce reusable AI architecture patterns; modular, abstracted, and configuration‑driven, to accelerate global adoption and facilitate cloud native swap out of components if necessary.
• Partner with engineering teams to deliver seamless integration, deployment and operationalisation.

Global Delivery & Deployment

Build scalable deployment patterns and operational playbooks that enable consistent multi‑market adoption. Ensure solutions adapt to local regulatory, data sovereignty and operational requirements.

Innovation & Experimentation

Evaluate emerging AI technologies and identify high‑value opportunities, driving continuous improvement in engineering practices, AI Adoption, automation and tooling.

Governance & Quality

Ensure all AI solutions comply with global standards, regulatory requirements and company risk frameworks embedding explainability, responsible AI practices and governance controls throughout the lifecycle. Produce clear high‑quality technical documentation, design artefacts and operational runbooks to support transparency, auditability and ethical standards.

Cross‑Functional Collaboration

Support adoption of shared AI capabilities by ensuring alignment to common delivery and architecture patterns.

Capability Building

• Model engineering excellence and champion best practices in modern AI development and delivery. Mentor engineers, share knowledge, administer knowledgebase and build a strong internal AI community across teams.
• Define and evolve engineering standards, coding practices and delivery frameworks to uplift capability providing technical expertise to strengthen the robustness and quality of initiatives

Performance Monitoring & Reporting

Define and track KPIs to measure model and system performance, reliability and business impact, establishing and maintaining: monitoring, alerting and performance dashboards as part of overall solutions.

What Success Looks Like

• Ensure systems comply with local regulations and licensing providing regulatory confidence.
• AI models and pipelines are performant, reliable and delivering measurable business impact.
• Solutions are deployed smoothly into production with strong observability, low latency and predictable cost with seamless system integration.
• Stakeholders clearly understand system performance through concise, data‑driven reporting.
• Engineering teams can easily adopt and extend shared AI capabilities due to clear patterns, documentation and support.
• AI features ship on time, operate at scale meeting security, compliance, and quality standards.
• Measurable KPIs show consistent improvement and solutions lead to tangible gains such as increased successful trips and vehicle utilization.
• Proactive rather than reactive issue identification with propose enhancements to initiatives and designs to resolve.

Minimum Education/Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence or a related technical field
• 3+ years of hands-on experience in AI/ML model development, generative AI, AI Agents and Data science solutions.
• Experience in leading end-to-end AI projects from ideation to deployment.
• Experience working with distributed teams and offshore delivery models
• Familiarity with data privacy laws (e.g. GDPR, CCPA) and global compliance frameworks
• Experience in agile product development and cross-functional collaboration