Top AI Jobs in Singapore

Lead MLOps efforts by designing, building, and maintaining ML pipelines, implementing CI/CD workflows, mentoring engineers, and ensuring data privacy and compliance.

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

Develop, train, fine-tune, evaluate, and deploy AI/ML models and LLM-powered data products. Build end-to-end model pipelines, feature engineering, monitoring, and production deployment. Apply generative AI for summarization, classification, knowledge extraction and semantic search. Collaborate with product, engineering, and business teams and integrate third-party tools and open-source models.

Customer-facing senior solutions engineer driving technical sales and adoption of Cloudera's Enterprise AI portfolio. Architect AI solutions, build training pipelines and large-scale experiments, demonstrate value to C-levels, consult with researchers, advocate customer feedback, and lead external evangelism. Own the technical sales lifecycle through post-sales success and renewals.

Embed with strategic customers to prototype, build, and productionize full-stack AI and agentic applications; advise on AI strategy, codify repeatable architectures and playbooks, mentor teams, and feed product feedback to improve enterprise AI platforms.

Design, develop, and deploy production-ready AI applications and scalable DataOps/MLOps pipelines for avionics manufacturing. Build LLM/RAG/agent-based solutions, integrate enterprise and manufacturing systems, implement CI/CD and containerized deployments, ensure security/compliance, and collaborate with stakeholders to deliver measurable operational improvements.

Lead development and delivery of AI products for Market Intelligence. Partner with investors and compliance to define use cases, translate needs into product requirements, manage end-to-end lifecycle, ensure model governance, and collaborate with data engineering for reliable pipelines and integrations.

Support finance transformation by converting policies and training into AI-enabled e-learning, creating digital learning content, developing Power BI dashboards, performing data extraction/cleansing/analysis, identifying automation opportunities with Power Platform and Copilot, and assisting AI agent proof-of-concepts while collaborating with finance, operations and technology teams.

Partner with Enterprise AEs to identify, qualify, and close AI/BI opportunities; engage C-level and analytics leaders to modernize analytics; displace legacy BI with governed, conversational AI-powered analytics; lead semantic governance and natural-language access conversations; drive proof-of-value pilots and scale enterprise adoption while enabling field teams.

Design, build, and scale data infrastructure for LLM training, fine-tuning, evaluation, and RAG. Implement and maintain data architectures, pipelines (batch and streaming), org-level IAM and governance, cost/performance optimizations, analytics/dashboarding, AI-assisted ops and internal automation tools, and support multi-cloud production workloads.

Manage AI Hub product backlogs and translate business challenges into user stories and scalable workflows. Partner with product, engineering, data, and operations teams to deliver AI/ML and automation solutions from experimentation to production. Drive testing, validation, adoption, metrics tracking, stakeholder engagement, and ensure adherence to governance, risk, and responsible AI practices.

Lead and build BNY's Singapore Applied AI team to identify, design, and deliver production-grade AI/ML and LLM solutions. Drive end-to-end delivery from problem framing, prototyping, evaluation, deployment, monitoring, and adoption while embedding responsible AI, governance, and cross-functional stakeholder alignment.

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

Own end-to-end architecture for AI-powered developer tooling: define technical vision, design LLM/agentic systems (RAG, tool-calling, orchestration), ensure scalability, security, and compliance, evaluate models/vendors, prototype with AI tools, and guide engineering teams to production.

Operate and optimize multi-cloud GPU inference platform for LLMs, build API services and CI/CD, manage AI clusters with IaC, improve observability, automate operations and build AI-assisted internal tools.

Translate business problems into data solutions: collect/explore data, build and evaluate statistical and AI models, deploy models as code (microservices), develop AI agents and RAG solutions using vector/graph databases, apply 3D/CAD and mesh algorithms, and support data governance, migration, and policy to maintain model lifecycle and business value.

As an AI Intern, develop an Agentic AI framework for requirement normalization, enhance Generative AI models, and document findings. Collaborate in research and implementation using machine learning and natural language processing.

As a Robotics AI Engineer Intern, you'll work on humanoid robotics software features, specializing in Vision-Language Model integration, simulation environment setup, or 3D Gaussian Splatting. You'll collaborate with senior engineers, develop pipelines, and configure digital twins.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

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 design and scaling of autonomous multi-agent AI outreach systems: define strategy and architecture, implement RAG and persistent memory, optimize LLMs, integrate with CRM and data pipelines, enforce privacy and safety guardrails, measure KPIs, and build/lead cross-functional AI and growth teams.

Design end-to-end AI-driven products for consumer and enterprise contexts. Conduct rapid research, prototype agent behaviour and personality, create polished UI/UX and brand-aligned agent experiences, and collaborate closely with front- and backend AI engineers to build testable, trustworthy AI solutions.

Design and build AI agent applications, optimize retrieval systems, collaborate with teams, and explore new AI intelligence forms.

responsible for driving AI initiatives from scoping to rollout, collaborating across teams to integrate AI in business workflows, tracking progress, and solving blockers proactively.

Maintain, design, and enhance the Group AI and model inventory system to support AI governance. Implement process improvements, integrate automation, collaborate with model owners, risk, compliance, IT and external reviewers, and drive future-state system implementation and regulatory compliance for Generative and Agentic AI models.