Top AI Jobs in Singapore

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.

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.

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.

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.

Lead and scale an APAC specialist sales team to drive adoption of AI-driven experience orchestration. Define engagement models with AEs, influence go-to-market and territory strategy, support high-value enterprise deals, translate customer challenges into scalable AI use cases with measurable business outcomes, and collaborate cross-functionally to refine product and messaging.

Lead strategic execution for BJAK's finance super app expansion: establish operating rhythms, coordinate cross-functional workstreams, prepare leadership materials, identify risks, drive decision-making, and own high-impact special projects to turn strategy into measurable progress.

Operate in the CEO Office to translate CEO priorities into execution across product, hiring, licensing, partnerships and launches. Track OKRs, decisions, blockers and deadlines; prepare briefs, updates and action trackers; work cross-functionally and resolve urgent business problems that span teams.

Lead product direction and roadmaps for major finance super-app areas; guide PMs, designers and engineers across squads; translate regulatory and operational constraints into pragmatic product decisions; drive launch quality, post-launch learning and continuous improvement.

Own product areas within a finance super app: define problems, scope, metrics and roadmap; work with engineering, design, data, operations and compliance; launch, monitor and iterate; simplify complex financial products into usable journeys.

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.

Lead development and implementation of AI governance frameworks, create playbooks and training, validate standards across business units, industrialise governance tooling, run bank-wide governance processes, and advise stakeholders on responsible AI practices.

Design, build, and maintain network automation, monitoring and AI-driven automation solutions (LLMs, RAG, vector DBs). Develop software for predictive analytics and NLP, deploy CI/CD pipelines, integrate APIs, automate network provisioning, troubleshoot systems, create monitoring dashboards, and support audits and regulatory requirements.

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.

Lead the AI Fluency programme, engaging stakeholders, delivering training, ensuring governance, and managing a team for technology adoption in the organization.

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.

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

Lead the development of AI and ML solutions for underwriting, manage a team of data scientists, and ensure compliance and governance in initiatives.

As an AI Specialist Solution Architect, you will architect AI systems, support technical sales, engage stakeholders, and operationalize AI while advocating for Red Hat products.

The Senior AI Architect will design and implement AI workloads, mentor teams, engage with customers, and drive AI methodologies across APAC while collaborating with sales and engineering teams.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Lead transformation of consumer credit risk by designing and deploying production-grade Agentic AI, data and automation (ADD) solutions to automate underwriting and decisioning, define AI-enabled operating models, align frameworks with risk appetite and regulations, and influence senior stakeholders to deliver sustainable outcomes across unsecured, mortgage, auto and wealth portfolios.

Design, build, and deploy AI/ML solutions (including generative models, RAG, and agentic AI) using large-scale structured and unstructured data. Collaborate with cross-functional teams to productionize models with cloud and MLOps, create data visualisations and insights, and apply modern ML techniques to improve customer experience and operational efficiency.