Top AI Jobs in Singapore

Join the R&D innovation team to develop web software and APIs, integrate frontier LLMs into automotive applications, ensure performance and scalability, and perform testing, debugging, and optimization of software modules.

The AI Product Manager Intern will analyze product data, design AI conversation flows, support experimentation, and report findings to stakeholders, focusing on AI-driven product features.

Design and ship a 14-domain skills taxonomy and SDK/CLI tooling, operate the MCP server, integrate agent protocols, improve LLM grounding for crypto, run developer hackathons, and produce technical content to grow the ecosystem.

Design and develop GenAI solutions for applications and tools, collaborate with teams, optimize AI pipelines, and document workflows.

Lead product planning and solution design for an AI-native engineering platform that integrates AI Agents, AI coding, CI/CD, testing, deployment, and knowledge systems to improve engineering productivity. Translate multi-role engineering workflows into measurable product capabilities, drive integration with code repositories and DevOps tools, evaluate product effectiveness, and scale adoption from pilot to enterprise-wide rollout.

Develop and integrate vision-guided perception with robotic control to enable autonomous maintenance tasks. Design decision-making, motion and path planning, safety constraints, simulation/testing frameworks, and validate solutions in field proof-of-concepts with service teams.

Lead Forward Deployed Engineering across APAC to design and deliver hybrid-cloud, Kubernetes-based AI platforms; bridge regional customer needs with global R&D and open-source communities; architect secure, compliant sovereign AI deployments; mentor senior engineers; drive upstream open-source contributions and regional developer engagement.

Lead field-facing engineering to integrate, prototype, and productionize enterprise and sovereign AI platforms. Architect secure, scalable Kubernetes-based deployments, optimize hardware-accelerated inference, upstream field innovations to open-source, mentor engineers, and drive global product feedback loops and SDLC improvements.

Lead AI strategy and commercial engagements for enterprise clients. Act as C-suite advisor, drive AI adoption, presales, solution design, and revenue growth. Develop AI use cases, PoCs, LLM/Generative AI solutions, partnerships, and go-to-market strategies while collaborating with product, engineering, and delivery teams and mentoring internal teams.

Lead a team to design, develop, and deploy reinforcement-learning and agent-based systems for games. Define technical roadmaps, drive R&D across RL/imitation/planning, establish evaluation frameworks, scale training and production pipelines, and collaborate cross-functionally to integrate robust, generalizable autonomous agents.

Conduct research and design efficient, GPU-accelerated generative AI models (LLMs, vision, robotics), publish at top conferences, mentor interns, collaborate across teams, and transition research into products.

Embed with enterprise engineering teams to deploy and scale agentic LLM workflows (Devin), drive adoption through workshops and pair-programming, quantify productivity impact, create playbooks and enablement materials, and turn engagements into repeatable deployment models.

Own QA and evaluation systems for multilingual AI agents: build evaluation gates, AI-driven localization testing, annotation/eval infrastructure, and feedback loops to auto-publish translations safely at scale. Lead cross-functional delivery across engineering, AI, linguists, and design to turn subjective quality into measurable, automatable systems.

Design, build, and maintain scalable data pipelines, data models, feature stores, and data catalogues to support AI model training and inference. Ensure data quality, lineage, governance, and secure integration of diverse real-time and batch transport data sources. Collaborate with AI architects, infrastructure engineers, and stakeholders to deliver production-grade data products and maintain pipeline reliability.

Design, provision, and operate cloud and hybrid infrastructure for AI workloads. Build CI/CD and IaC automation, manage GPU and Kubernetes environments, implement observability, ensure security/compliance, support MLOps workflows, and coordinate deployments and incident response with partners to support live transport operations.

Design, build, and deliver end-to-end AI-enabled applications including retrieval pipelines, LLM orchestration, backend APIs and user interfaces. Own SDLC, CI/CD, containerisation, observability and secure cloud deployments. Integrate LLM APIs and vector DBs, evaluate models, contribute shared platform components, and work with business teams and AI PMs.

Lead product strategy and delivery for AI-enabled workflow products across design, tendering, construction management, and document intelligence. Define vision, roadmaps, requirements, KPIs, and partner with engineers, data teams, designers, and stakeholders to launch and iterate production AI features with governance, explainability, and human-in-the-loop tradeoffs.

Build, test and sustain AI agents, RAG/vector search and platform integrations for ETP Hub. Develop retrieval, embedding, indexing and user-facing AI tools, create API connectors and automation scripts, document workflows, and support deployment, monitoring and refinements to improve search, retrieval and operational efficiency.

Lead technical architecture and delivery for AI automation, data integration, workflow automation, and platform capabilities. Design scalable, secure solutions, manage AI engineering delivery, set engineering standards, oversee data and inference-layer work, integrate SaaS/platforms, and establish governance, testing, and documentation for sustainable automation.

Lead and deliver agentic AI solutions for networking: design RAG and agent architectures, oversee development and deployment, mentor engineers, establish MLOps/DevOps pipelines, optimize performance and governance, and collaborate with networking stakeholders to drive AI strategy and operationalize network automation and anomaly detection.

Lead development of cost/benefit methodologies and ROI projections for AI agents; quantify productivity gains and cost reductions; design hybrid human–AI workforce models; redefine job structures, performance management, and incentives to optimize AI-human collaboration; partner with LOBs and HR for adoption and workflow redesign.

Develop and deploy enterprise-grade AI and Generative AI solutions, working on applications like LLM and RAG, ensuring operational stability.

Embed with enterprise clients to design, build, and operationalize agentic AI solutions in production: architect integrations with ERP and identity systems, write production-grade code, build retrieval pipelines, agent orchestration, guardrails, evaluation suites and observability, manage cutover and handover to support, and produce reusable patterns and accelerators.

Design and build company-wide AI knowledge infrastructure and context layers, develop scalable LLM application architecture (RAG, vector DBs, prompt workflows), own end-to-end delivery of internal AI tools, optimize performance and inference, evaluate/integrate AI tooling, and mentor junior engineers while translating business needs into technical roadmaps.

The Director of AI is responsible for solving complex AI challenges, exceeding client expectations, leading AI teams, and fostering ethical AI practices. They will create thought leadership and manage AI solutions from conception to commercialization while building client relationships and partnerships.