Enterprise Solution Architect (AI Engineering)

An exciting opportunity awaits at Changi’s Digital Technology team, driving the digital transformation of Changi Airport Group. As head of a team of several in-house and offshore resources in an agile, cross-functional group, you will lead our AI engineering capability, driving the strategy, architecture, delivery, and operations of AI-powered products, platforms and digital services across commercial and airport operations domains. Build and lead a high-performing team, scale AI-assisted engineering practices, establish enterprise AI platforms and guardrails, and deliver measurable business outcomes through responsible adoption of generative AI, agentic systems and emerging technologies.

Responsibilities

• Product and stakeholder partnership: Partner with Product Owners, UX designers, business stakeholders, and engineers to translate business challenges into scalable AI-powered products and services.
• Technical strategy and standards: Establish architecture principles, engineering standards, and reusable enterprise capabilities for AI-native products, while guiding build-versus-buy decisions across AI platforms, SaaS offerings, and custom solutions.
• Solution architecture and planning: Oversee the architecture of AI-enabled solutions that integrate large language models (LLMs), AI agents, SaaS products, cloud-native AWS services, internal and external data sources, and enterprise knowledge bases. Structure delivery plans and provide credible effort, resource, and timeline estimates.
• Engineering leadership and delivery: Lead a multidisciplinary team of full-stack developers and project managers, including offshore resources, to design, build, operate, and continuously improve AI-powered, web-based, and customer-facing digital applications.
• Production readiness: Ensure AI solutions are production-ready through appropriate guardrails, fallback mechanisms, human-in-the-loop workflows, testing, release controls, and operational support.
• Reliability and operations: Establish AI-enabled observability, monitoring, and incident-management practices to accelerate troubleshooting and deliver reliable, resilient, and scalable services.
• AI lifecycle management: Oversee the lifecycle and continuous improvement of models, prompts, retrieval pipelines, agents, and related AI components from experimentation through production.
• Hands-on technical leadership: Provide direct technical leadership on complex, high-risk, or cross-system issues, supporting root-cause analysis and continuity of critical services.
• People leadership and capability building: Recruit, lead, mentor, and develop full-stack engineers and project managers; strengthen technical depth, delivery discipline, succession readiness, and collaboration across in-house and offshore teams.
• Engineering productivity: Champion responsible use of AI-assisted development tools to improve engineering productivity, software quality, and consistency across the end-to-end delivery lifecycle, including offshore delivery.
• Culture and community: Foster a culture of experimentation, learning, accountability, and innovation. Promote knowledge sharing and scale proven tools, patterns, and services across CAG’s developer community.
• Technology thought leadership: Help the organisation distinguish practical opportunities from hype, define what good looks like in the age of AI, and create differentiated experiences for end users.
• Planning and commercial management: Develop the annual workplan, manage team budgets and capacity, and oversee the performance of offshore partners and technology vendors.
• Business outcomes: Define measurable success criteria and track the business value, adoption, operational impact, and benefits realised from AI initiatives.

Requirements

• Experience: 10 or more years of experience in software engineering, solution architecture, platform engineering, or related technology roles, including at least 5 years leading engineering teams and delivering complex, business-critical digital products.
• Education: Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Science, or a related discipline, or equivalent practical experience. A postgraduate qualification is an advantage.
• Software and solution architecture: Strong foundation in modern software engineering and distributed-system design, with hands-on proficiency in Python and working knowledge of JavaScript or TypeScript, APIs, event-driven architectures, relational and NoSQL databases, and customer-facing web applications.
• Cloud and platform engineering: Substantial experience designing and operating secure, scalable solutions on AWS. Experience with relevant services such as Bedrock, AgentCore, Lambda, API Gateway, ECS or EKS, S3, RDS, DynamoDB, Cognito, SNS, SQS, CloudWatch, and IAM is desirable.
• AI engineering: Demonstrated experience designing and delivering production-grade generative AI solutions using large language models, retrieval-augmented generation, embeddings, vector search, tool or function calling, and agentic workflows.
• AI lifecycle and evaluation: Strong understanding of prompt, model, retrieval, and agent lifecycle management, including versioning, automated evaluation, regression testing, grounding, guardrails, red-teaming, human oversight, and continuous improvement.
• DevSecOps and infrastructure: Practical experience with infrastructure as code, preferably AWS CDK or Terraform; CI/CD; automated testing; release management; secrets management; and secure software-supply-chain practices.
• Reliability and cost management: Experience establishing production observability, service-level objectives, incident-management practices, capacity planning, and optimisation across performance, latency, reliability, and AI consumption costs.
• People leadership: Proven ability to recruit, lead, coach, and develop high-performing multidisciplinary engineering teams.
• Delivery and commercial management: Strong track record of translating strategy into executable roadmaps, making pragmatic build-versus-buy decisions, estimating delivery effort, managing budgets and capacity, and holding vendors and delivery partners accountable for outcomes.
• Stakeholder leadership: Excellent communication, facilitation, and influencing skills, with the ability to explain complex technical trade-offs to senior leaders.
• Learning agility: Strong judgement and intellectual curiosity, with the ability to evaluate rapidly evolving AI technologies, distinguish durable capabilities from hype, and apply emerging approaches responsibly in an enterprise environment.
• Preferred: Experience in aviation, transport, customer loyalty, large-scale consumer services, or other operationally complex environments.