Top AI Engineer Jobs in Singapore

Design, build, and deploy end-to-end production AI applications for smart manufacturing: data pipelines, model training/inference, APIs, web/mobile front-ends, digital twins, robotics integration, containerized edge/cloud deployments, GPU inference optimization, Responsible AI, and 3D simulation validation.

Design, build, deploy, and support end-to-end AI applications for manufacturing: data pipelines, feature engineering, model training/inference, web UIs/APIs, containerized deployments, LLM/RAG integrations, MLOps, observability, and Responsible AI practices.

Design, build, and ship agentic AI applications that answer business questions over governed data. Implement RAG, NL-to-query, tool/function calling, and evaluation/guardrails. Partner with data teams on metrics, models, pipelines, and apply strong data governance and security for financial services.

This internship involves assisting in machine learning development, AI automation, data engineering, and exploring innovative projects to enhance test efficiency and engineering productivity.

Lead design and delivery of end-to-end AI solutions for smart manufacturing, including data pipelines, ML/DL models, LLM and agentic AI integration, full-stack front-end/back-end apps, robotics and AMR integration, digital twin and 3D simulation, containerized cloud/edge deployment, CI/CD, observability, and team mentorship.

The role involves developing AI and ML solutions for semiconductor workflows, training Citizen Data Scientists, and integrating intelligent agents into engineering processes.

The Principal AI Engineer leads the design and operationalization of complex AI systems, architecting multi-agent solutions while ensuring safety, reliability, and performance. They mentor engineers and improve processes and products based on measurable outcomes.

Lead design, build, and operationalize agentic AI platforms and services. Develop orchestration, evaluation, observability, governance, APIs, and backend systems. Improve performance, scalability, and reliability. Provide technical leadership, mentor engineers, and collaborate with product, data science, security, and governance teams to ensure responsible AI deployment across the enterprise.

Lead design and delivery of generative AI agents and architectures, build robust APIs and scalable systems, roadmap AI applications, collaborate cross-functionally, troubleshoot production issues, and drive internal efficiency and customer-facing AI solutions.

Lead end-to-end design and implementation of generative AI solutions and APIs to improve customer-facing and internal automation. Build scalable, high-concurrency backend systems, collaborate with cross-functional teams, set best practices, and mentor engineers while driving deployment and MLOps for production AI products.

Design, build, and ship end-to-end production AI applications for smart manufacturing: data pipelines, model training/inference, APIs, web/mobile front-ends, on-prem/edge deployment, MLOps, monitoring, Responsible AI, and integration with robotics and MES.

The role involves developing AI-driven solutions for semiconductor engineering, focusing on GenAI systems, data pipelines, and machine learning models while collaborating with cross-functional teams.

The role involves driving technical sales strategy for AI solutions, consulting on data architectures, and presenting to C-level executives, requiring extensive customer-facing experience and AI proficiency.

Lead design and implementation of cloud DevOps strategy for global Data and AI infrastructure. Build observability and detection capabilities, enable self-service tooling for data teams, manage scalable, secure infrastructure, and align DevOps practices and standards across geographies.

As a Solutions Architect at CoreWeave, you'll lead the technical relationship with customers to optimize AI workloads on cloud infrastructure, focusing on Kubernetes and HPC environments.

Develop and implement computer vision and machine learning models (object/change/anomaly detection, classification, behavior analysis, video stitching). Build POCs, run experiments in realistic environments, optimize model robustness, produce documentation and patentable ideas, stay current with AI advances, present findings, and (for senior profiles) provide technical mentorship.

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.

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 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.

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

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.

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.

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.

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.