Top AI Engineer Jobs in Singapore

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 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, 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 Senior AI Engineer designs and develops scalable AI/ML systems, operationalizes models, ensures reliability and observability, and mentors junior engineers.

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.

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

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.

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.

Lead AI Engineer will design and deploy advanced AI and agentic LLM systems, lead and mentor an AI/data engineering team, build MLOps pipelines and cloud deployments, produce client proposals and technical thought leadership, and drive model lifecycle, evaluation, and responsible-AI practices.

Perform structural FEA using commercial CAE tools to evaluate static, dynamic, thermal, and fatigue behavior. Build models, apply materials/boundary conditions, run simulations (including explicit dynamics), validate with hand calculations and test correlation, support root-cause failure analysis, and produce technical reports while collaborating with design, test, and manufacturing teams.

Perform CFD and thermal simulations for electronics cooling at component, board, enclosure, and subsystem levels. Set up models, run steady-state and transient analyses, post-process results, correlate with test data, support V&V and certification needs, and document findings. Learn mesh strategy, solver settings, and collaborate with cross-functional teams under senior engineer guidance.

Perform FEA simulations (linear/nonlinear, dynamic, thermal, fatigue) using commercial CAE tools, build validated models, correlate results with tests, support V&V and failure investigations, document findings, and collaborate with design, test, and manufacturing to improve designs and workflows.

The Senior AI/ML Engineer will design, develop, and deploy language models for African languages, focusing on NLP applications and cultural context.

The AI Engineer supports the global AI sandbox, develops AI solutions, conducts R&D, and collaborates with cross-functional teams to deliver high-quality AI products.

As an Applied AI Engineer, you will develop AI features, improve model behavior, debug production issues, and collaborate on systems that deliver reliable AI solutions.

Develop AI solutions leveraging LLMs, lead development lifecycle, build scalable systems, collaborate across teams, and present to stakeholders.

The AI Engineer will develop AI solutions leveraging LLMs, leading the development lifecycle and collaborating with teams to integrate AI into workflows.

The Senior AI Engineer will develop a retrieval layer for enterprise AI agents, ensuring relevant, permission-aware responses by integrating multiple data sources and designing hybrid retrieval systems. Responsibilities include mentoring, designing pipelines, and building evaluation systems, with a strong focus on retrieval quality and permission management.

The AI Engineer will develop and deploy AI/ML solutions, focusing on large language models and machine learning to solve engineering challenges, integrating tools into existing workflows, and collaborating with various specialists.