Dyson Hiring! Full Time Lead AI Engineer in Johor - Ricebowl

Job Description

Join Dyson’s global IT data community and play a key role in designing, building, and scaling enterprise-grade AI systems. You’ll lead technical delivery across squads, working closely with data analysts, product managers, data scientists, and AI/ML Ops engineers to deliver robust AI solutions that drive business impact at scale.

WHO YOU ARE

You are a highly experienced, hands-on AI engineer who thrives on solving complex problems with scalable, production-grade solutions. You are passionate about building AI systems that move beyond prototypes into robust, observable, and governed platforms that deliver measurable business value.

You demonstrate strong ownership and technical leadership, setting direction for engineering practices while remaining deeply involved in implementation. You are comfortable leading cross-team initiatives, guiding architectural decisions, and raising the overall engineering bar.

You prioritize continuous learning, whether it is exploring new frameworks, experimenting with novel architectures, or deepening your understanding of AI infrastructure. You actively drive adoption of best practices across teams while helping others grow their technical capability.

WHAT YOU'LL DO

As a Lead AI Engineer you will:

• Lead the design and delivery of robust, scalable AI systems and agentic workflows across squads
• Define architecture patterns for orchestration, multi-step reasoning, tool use, and agent coordination
• Set and enforce standards for context engineering, including prompt design, retrieval augmentation, grounding, and memory systems
• Oversee the development of modular, reusable APIs, services, and integration layers that enable enterprise AI delivery
• Establish evaluation frameworks, quality gates, and continuous improvement loops to ensure system reliability and performance
• Own production monitoring and operational excellence for AI systems, including tracing, observability, debugging, and incident response patterns
• Lead technical decomposition of complex and ambiguous business problems into well-scoped, solvable AI engineering workstreams
• Mentor and guide engineers through design reviews, code reviews, technical planning, and best practice adoption
• Collaborate closely with Data Science, AI/ML Ops, Platform, and Product teams to ensure scalable, secure, and governed delivery
• Define reusable components, frameworks, and engineering standards that accelerate AI solution delivery across teams
• Partner with stakeholders to translate business needs into scalable AI architectures and solutions
• Share learnings, develop standards, and educate the wider business on responsible and effective use of AI

JOB REQUIREMENTS

• Programming and software engineering depth. Strong Python proficiency and a proven track record writing production-grade code, designing APIs and services, and leading code and design reviews across squads. Comfortable with cloud-native deployment (i.e. GCP), containers, and CI/CD.
• Applied ML and GenAI fluency. Strong grasp of ML and LLM concepts (embeddings, retrieval, prompting, fine-tuning, evaluation), with the judgement to pick the right model, framework, or pattern for each workload.
• AI system design and solution architecture. Able to design end-to-end AI systems, defining boundaries, data flows, integration points, and scalability strategies that turn ambiguous problems into reusable, production-ready patterns.
• Agentic AI and orchestration. Hands-on experience designing agentic workflows involving planning, tool use, multi-step reasoning, memory, and multi-agent coordination, including MCP-based patterns and orchestration frameworks (e.g. LangGraph).
• Data engineering for AI. Solid understanding of the data foundations behind AI, including pipelines, ingestion and preprocessing, vector stores, and data quality and lineage, ensuring retrieval and grounding run on reliable, governed data.
• Context engineering. Able to set standards for RAG, grounding, memory systems, and prompt patterns so AI outputs are accurate, faithful, and enterprise-grade.
• Evaluation, observability, and AgentOps. Experience establishing evaluation frameworks, quality gates, tracing, monitoring, and cost and latency controls, using tooling such as Langfuse, plus incident response patterns for AI in production.
• Platform collaboration and deployment. Able to integrate AI solutions cleanly with existing platforms, working with AI/ML Ops, Platform, Data Science, and Product teams to ensure secure, governed delivery.
• Task decomposition and delivery ownership. Strong analytical thinking to break complex, ambiguous problems into well-scoped workstreams, with proven ownership from concept to production.
• Technical leadership and continuous learning. Able to mentor engineers, guide architectural decisions, and raise the engineering bar. Quick to adopt new frameworks and AI-assisted coding tools (e.g. Claude Code, Codex, Cursor) while critically validating their outputs.