Top AI Engineer Jobs in Singapore

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflow systems, APIs, orchestration, retries, fallbacks, and failure handling. Make architectural decisions, lead a small senior engineering team, mentor engineers, and establish strong observability, monitoring, testing, security, and operational practices. Partner with ML leadership to convert model capabilities into scalable, reliable product experiences while balancing latency, cost, reliability, and user experience.

Lead the engineering strategy for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build production APIs, make architectural trade-offs, establish observability and testing foundations, and partner with ML leadership. Lead a small senior engineering team through technical reviews, mentorship, and hands-on development while improving system reliability, latency, scalability, and user experience.

Own product areas within BJAK’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product strategy, roadmaps, metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; and use data, research, feedback, and production insights to launch, monitor, and iteratively improve products.

Own product areas for AI stockbroking and other financial services, from identifying user problems through roadmap definition, feature launches, and post-launch iteration. Partner with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, establish success metrics, analyze feedback and performance, and balance speed, usability, business impact, compliance, and operational complexity.

Own AI Investing product areas across financial services, defining user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial workflows. Use research, feedback, experiments, metrics, and production insights to launch, monitor, and iteratively improve products while balancing speed, user experience, business impact, compliance, and operational complexity.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, compliance, operations, and business teams; simplify complex financial workflows; launch features; monitor performance; and drive continuous iteration using data, feedback, research, and production insights.

Build and operate AI agents, assistants, knowledge-base pipelines, APIs, integrations, and user-facing tools for company-wide AI adoption. Own features from design through production, including testing, CI/CD, observability, security guardrails, responsible AI, metrics, documentation, incident response, and continuous improvement based on internal user feedback.

Design, build, secure, and operate highly available data centre networks, including leaf-spine fabrics, routing, switching, firewalls, load balancers, segmentation, and inter-data-centre connectivity. Troubleshoot mission-critical network issues, improve reliability, and manage capacity and performance. Develop Network-as-Code automation for provisioning, validation, testing, compliance, and drift detection. Apply telemetry, AI, and AIOps techniques while collaborating with cloud, compute, storage, cybersecurity, and application teams.

Develop innovative AI web applications and APIs with the R&D innovation team. Integrate frontier large language models into automotive software, design scalable and usable applications, and perform testing, debugging, and performance optimization. The role also involves applying knowledge of robotics, sensors, actuators, and perception technologies, with opportunities to use ROS/ROS2, Docker, humanoid robotics, and navigation algorithms.

Architects and scales manufacturing data ecosystems, advanced analytics, machine learning models, agentic workflows, and intelligent automation pipelines. Develops production-grade solutions for yield optimization, predictive maintenance, and operational efficiency using Python, R, SQL, and modern data platforms. Partners with manufacturing, supply chain, engineering, and leadership teams to translate complex data into strategic decisions. Leads multidisciplinary teams and establishes best practices for AI adoption and data products.

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.

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

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.

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.

Design and build an AI commerce platform: model routing decision engine, compute pricing and billing, reliability and microservice infrastructure, model evaluation and pricing data pipelines, caching and low-latency performance engineering, frontend and backend full-stack delivery through deployment.

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.

Develop and adapt advanced multimodal AI and robotics models (e.g., VLA, SLAM, navigation). Build training pipelines, simulation-based data generation, large-scale model training, experiments, evaluations, and contribute to publications and open-source efforts.

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

Design, build and ship native Android features in Kotlin for an AI-enabled neobank: implement financial user flows, integrate REST APIs, optimize performance and reliability, use analytics and production feedback, and add on-device AI where it improves UX.

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.

Build and ship native Android features for an AI-enabled neobank using Kotlin. Design clean user flows, integrate backend APIs, improve performance and reliability, debug production issues, and implement AI-assisted on-device experiences where beneficial.

The Full Stack Engineer will develop AI-driven features, design workflows, ensure system reliability, and collaborate with cross-functional teams to integrate AI capabilities into chat applications.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

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.