Top AI Engineer Jobs in Singapore

Develops secure UI and backend services enabling compliant enterprise AI and LLM access for China-based users. Responsibilities include designing APIs and applications, integrating approved LLM endpoints, implementing policy enforcement and access controls, producing architecture artifacts, maintaining AWS services, analyzing operational telemetry, and improving resiliency, security, and coding practices. The role also promotes responsible AI-assisted development, testing, automation, and agile software delivery.

Provides senior technology consulting and technical leadership for HPC and AI deployment solutions. Designs enterprise architectures, leads cross-functional implementation teams, resolves complex multi-vendor technology issues, advises senior customer IT leadership, and supports proposals and sales activities. Develops reusable solutions, creates project plans and architecture specifications, presents at technical conferences, mentors consultants, and cultivates strategic customer relationships.

Designs and delivers agentic AI systems that transform complex business processes into autonomous, scalable workflows. Responsibilities include process decomposition, AI architecture, agent and tool orchestration, prompt and context strategy, prototyping, security controls, human-in-the-loop design, testing, cross-functional delivery, value measurement, and mentoring. The role requires collaboration with business, architecture, security, platform engineering, and project management teams through production release.

Designs and delivers agentic AI systems that transform complex manual business processes into secure, scalable autonomous workflows. Responsibilities include process decomposition, AI architecture, agent and prompt design, integrations, prototyping, testing, human-in-the-loop controls, least-privilege security, cross-functional delivery, value measurement, and mentoring. The role requires strong business-process redesign experience alongside hands-on AI/ML and LLM system development.

Leads the redesign of complex business processes into autonomous, agentic AI systems. Responsibilities include process decomposition, AI architecture, agent workflow development, prototyping, integration, security controls, human-in-the-loop design, testing, production delivery, value measurement, and mentoring. The role collaborates with business, architecture, security, platform engineering, and project management teams to deliver scalable generative AI solutions.

Designs, develops, deploys, and governs AI-powered products, intelligent workflows, and autonomous multi-agent systems for HR and People organizations. Builds integrations across Workday, ServiceNow, Microsoft platforms, APIs, and enterprise data sources. Develops reusable AI frameworks, orchestration, tool calling, retrieval, monitoring, and evaluation capabilities while ensuring security, privacy, compliance, and responsible AI standards. Partners with business and technology leaders to identify transformation opportunities and deliver scalable solutions across recruiting, onboarding, employee services, learning, talent management, and workforce planning.

Build and operate enterprise-scale AI platforms, services, data pipelines, and ML workflows. Responsibilities include data ingestion, feature engineering, model deployment, evaluation, monitoring, governance, cloud migration, CI/CD automation, and observability. Collaborate with data scientists, product teams, and platform engineers to deliver secure, scalable production systems using Python, Spark, SQL, Hadoop tools, and cloud technologies.

Lead data-driven process and test integration initiatives to improve high-value-bits yield (PG2+). Develop models and monitoring for tool performance, equipment prognostics, and test optimization. Prototype solutions with cross-functional teams, deploy best-known-methods to production, and integrate AI-assisted tools to drive continuous improvement and operational excellence.

Build and deploy full-stack AI applications for smart manufacturing, including data pipelines, machine learning models, APIs, web interfaces, LLM integrations, automation tools, and observability. Develop containerized solutions with Docker and Kubernetes/OpenShift, contribute to CI/CD and MLOps practices, integrate robotics and manufacturing systems, and apply Responsible AI controls. Collaborate with data scientists, software engineers, and manufacturing stakeholders to improve operational decision-making and autonomous operations.

Design, build, and deploy production AI applications for semiconductor manufacturing. Responsibilities include developing full-stack machine learning solutions, APIs, web applications, data pipelines, LLM and agentic AI capabilities, analytics, and integrations with robotics, automation, control systems, and MES platforms. The role also leads deployment, observability, CI/CD, model evaluation, responsible AI, optimization, testing, mentoring, and technical documentation across global manufacturing environments.

Leads the architecture and delivery of production AI products for autonomous semiconductor manufacturing. Responsibilities span data engineering, machine learning, GenAI, full-stack applications, robotics integration, digital twins, simulation, and edge-to-cloud deployment. The role also owns CI/CD, observability, model evaluation, Responsible AI, performance optimization, technical mentorship, and cross-functional delivery across software, manufacturing, robotics, and infrastructure teams.

Develop scalable backend services and APIs that integrate generative AI capabilities, LLMs, and machine learning models. Build microservices, data flows, caching, and secure integrations while ensuring reliability through testing, monitoring, observability, and performance optimization. Collaborate with product, design, data science, and ML engineering teams in an agile environment. Mentor junior engineers, conduct code reviews, improve CI/CD practices, and contribute to evolving R&D initiatives.

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.

Participate in full-stack development and maintenance of Binance’s KYC system, including backend services, frontend features, documentation, testing, regression, troubleshooting, and customer issue resolution. The role is a 3–6 month accelerator internship for current university students and recent graduates, with an emphasis on Java, Spring Boot, databases, middleware, and frontend technologies.

Advises Singapore Government agencies on data modernization, AI transformation, analytics, governance, and cloud adoption. Designs secure enterprise data and AI architectures using Microsoft platforms, leads executive workshops and technical engagements, supports complex sales opportunities, and guides responsible AI adoption in regulated public-sector environments. Collaborates with sales, engineering, partners, and customer stakeholders while contributing reusable assets, technical readiness, and community expertise.

Designs and builds production-grade generative AI systems, including LLM architectures, fine-tuning pipelines, evaluations, RAG and multi-agent integrations. Optimizes models for latency, throughput, token efficiency, reliability, and cost. The role also develops enterprise integration, governance, monitoring, safety, and deployment patterns on AWS, while translating client requirements into model strategies and contributing hands-on production code, design reviews, and reusable engineering practices.

Serves as a trusted technical advisor for Microsoft cloud and AI solutions, leading customer engagements, technical strategy, solution demonstrations, proof-of-concept sessions, architecture discussions, and technical wins. Partners with account teams, marketing, engineering, and external partners to support project delivery, address capability gaps, gather competitive insights, and drive customer digital transformation, security improvements, and adoption of Microsoft technologies.

Design and build production-ready AI applications and secure backend integrations for an intelligent digital services platform. Develop retrieval pipelines using embeddings and semantic search, integrate LLM capabilities, establish monitoring and guardrails, evaluate vendor AI solutions, and ensure security, reliability, explainability, and graceful degradation. Collaborate across the platform architecture and own AI features from design through post-launch optimization.

Design and implement scalable LLM-powered applications, microservices, and APIs. Build AI agents, RAG-enabled semantic search, vector DB integration, CI/CD pipelines, and guardrails. Collaborate with stakeholders, own backend and full-stack development, and support DevOps, testing, and production reliability.

Early-career full stack internship developing frontend and backend features under senior guidance. Integrate AI coding tools into workflows, translate product requirements to implementations, contribute across the product lifecycle, and suggest cross-team process improvements while learning full-stack practices.

Develops AI-enabled cybersecurity solutions to improve the firm's security posture and enhance cyber risk products and processes. Responsibilities include analyzing requirements, designing and researching technical solutions, developing and testing software, enforcing SDLC practices, documenting procedures, supporting SRE and operations teams, and evaluating technologies for architectural review.

Design, build, and deploy production generative AI applications using RAG, agents, and multi-agent systems. Collaborate with AI/ML, software, product, design, and data teams to deliver scalable and reliable solutions. Make architectural decisions, optimize performance and cost, review code, apply cloud and LLMOps practices, and mentor junior engineers while contributing to engineering standards.

Leads AI engineering teams delivering scalable, production-ready generative AI and agentic solutions. Provides architectural and technical direction, oversees RAG pipelines, vector database integration, model optimization, testing, guardrails, responsible AI, monitoring, and documentation. Collaborates with product and business stakeholders, reviews designs and code, mentors engineers, and shapes technical roadmaps and engineering practices.

Lead the Kai generative AI engineering team by defining architecture, building reliable agentic systems, and delivering trusted AI products. Manage and mentor AI, backend, and frontend engineers while driving evaluation, safety, observability, scalability, security, and operational readiness. Partner with Product, Design, Data, Security, Infrastructure, and domain teams to translate business needs into secure customer experiences and reusable AI platforms.

Design, develop and roll out geospatial AI-centric MVPs and digital products. Work full-stack on frontend and backend, implement and fine-tune ML/GenAI/LLM models, use cloud and DevSecOps practices, collaborate with stakeholders in cross-functional teams.