Top AI Jobs in Singapore

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.

Architect and deploy autonomous Agentic AI systems for smart manufacturing. Responsibilities include developing multi-step AI agents, integrating LLMs with APIs and databases, building evaluation and profiling frameworks, implementing MCP and A2A interoperability, deploying agents through CI/CD to Kubernetes and cloud containers, securing agent workflows, optimizing inference performance, and mentoring engineers.

Designs and validates data center network architectures for AI infrastructure, including BGP, EVPN/VXLAN, RoCEv2, QoS, and RDMA fabrics. Builds qualification labs, tests switches, NICs, and network operating systems, investigates performance and interoperability issues, troubleshoots congestion and packet loss, and develops documented standards, automation, and product feedback for customer deployments.

Develops AI-enabled applications for digital air traffic control assistants by integrating LLMs, RAG, AI agents, APIs, databases, enterprise systems, and operational data. Builds backend services, user interfaces, connectors, evaluation and monitoring systems, and end-to-end prototypes. Collaborates with domain experts to define requirements and tests, and supports demonstrations, documentation, deployments, technical reports, publications, and industry or regulatory partnerships.

Leads learning and workforce transformation initiatives for the Technology business unit. Partners with senior stakeholders to identify capability gaps, design skills-based learning pathways, drive AI-enabled learning and productivity programs, measure business impact, and support digital adoption. The role bridges HR, technology, and business teams while managing governance, risk, stakeholders, and multiple learning workstreams.

Own end-to-end R&D delivery for a multi-region, 10,000+ GPU cloud platform. Responsibilities include integrated planning, scheduling, milestone and critical-path tracking, dependency and risk management, delivery-process improvement, program rituals, leadership reporting, escalation, and coordinating regional rollouts with engineering teams.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Leads technical sales engagements for strategic APAC professional services customers. Designs modern data platforms and AI-ready architectures using Microsoft Fabric, Azure Databricks, Azure databases, Purview, Azure AI, RAG, semantic search, and governed analytics. Influences enterprise architecture decisions, leads proofs of concept and workshops, resolves technical blockers, develops reusable industry solutions, and connects Microsoft Data + AI capabilities to secure production workflows and professional services outcomes.

Advise lawyers and legal teams across APAC on adopting Relativity’s AI platform. Lead discovery, demonstrations, onboarding, training, workflow transformation, and value realization across the customer lifecycle. Translate AI capabilities into practical legal use cases, support pre-sales and post-sales engagement, build trusted customer relationships, and provide feedback to Product, Engineering, and Marketing. The role requires an actively licensed lawyer with legal practice experience, strong litigation and regulatory investigation knowledge, and hands-on legal AI experience.

Own end-to-end delivery of AI and autonomous robotics inspection solutions for maritime and offshore clients. Lead technical discovery, solution design, project delivery, client advisory, stakeholder alignment, vendor collaboration, and user adoption. Direct computer vision, perception, sensor fusion, SLAM, anomaly detection, cloud AI, MLOps, security, and data governance practices while reviewing architecture, validating model performance, supporting proposals, and guiding production deployment.

Leads end-to-end client delivery for data engineering and AI engagements, converting statements of work into plans, backlogs, milestones, and governance. Manages agile engineering teams, client relationships, scope, budgets, risks, quality, AI evaluation, security and privacy approvals, release readiness, and production handover. The role also supports proposals, delivery playbooks, managed-service transitions, and regulated-sector validation requirements.

Lead identification, implementation and tracking of productivity, safety and sustainability initiatives on construction projects using automation, robotics and AI. Engage stakeholders to drive adoption, review contract specifications, and apply lean process improvements to enhance construction efficiency and environmental practices.

Lead technology audit engagements focused on enterprise data governance, data quality, AI governance, model validation, access controls, and technology risk. Assess governance frameworks, regulatory compliance, and control effectiveness; develop scalable data and AI audit testing programs; support integrated audits; communicate findings to senior stakeholders; and lead regional audit teams. Promote advanced analytics, automation, and AI/ML tools to improve audit effectiveness. Regional travel of 10%–20% is required.

Lead complex SaaS sales and digital transformation engagements for strategic financial services, construction, industrial, and energy customers. Responsibilities include account and opportunity planning, stakeholder engagement, deal strategy, negotiation, forecasting, pipeline management, and closing multi-year AI, CRM, ERP, and low-code platform initiatives. The role requires coordinating internal teams, partners, and services organizations while aligning solutions with customer objectives, budgets, and regulatory requirements.

Lead consultative sales of Microsoft AI Workforce solutions, including Copilot, AI agents, Viva, Windows 365, and Azure Virtual Desktop. Develop customer strategies, identify and close strategic opportunities, build stakeholder networks, forecast demand, and orchestrate internal teams and partners. Engage business and IT decision-makers, communicate solution value, manage competitive pursuits, and drive cross-sell, upsell, adoption, and AI transformation outcomes.

Leads enterprise workforce reinvention by designing AI-enabled operating models, deploying agents, developing new ways of working, and driving sustained behavioral adoption. Responsibilities include AI readiness assessments, literacy and retraining programs, performance and onboarding integration, AI councils and champion networks, global training, stakeholder workshops, privacy protection, and adoption measurement across large user populations. The role partners with business, technology, HR, operations, Microsoft account teams, and engineering specialists.

Develop AI research agents, datasets, benchmarks, and reproducible computational experiments at the intersection of artificial intelligence, finance, and social sciences. Responsibilities include implementing and evaluating machine learning and language models, reproducing empirical analyses, troubleshooting research software, analyzing results, and preparing reports and publication materials. The role collaborates with faculty and research partners on interdisciplinary research and benchmark development.

Leads operational technology and automation strategy for a biologics manufacturing campus. Oversees automation architecture, project delivery, budgeting, suppliers, validation, compliance, digital and AI enablement, performance management, and continuous improvement. Acts as a technical authority, supports FDA inspections and facility expansion, and develops automation engineering teams and culture while enabling smart factory and autonomous manufacturing capabilities.

Leads consultative discovery and solution development for AspenTech customers across Oil & Gas, Chemicals, Power, Mining, and Food & Beverage industries. Partners with sales teams to identify business challenges, demonstrate software capabilities, develop value propositions, create proposals and scopes of work, and support opportunities through solution design and delivery. Requires strong industry, technical, analytical, communication, and consultative expertise, with substantial regional travel across APJ.

Supports third-party risk and enterprise GRC system implementations through process documentation, data preparation, user onboarding, testing, reporting, stakeholder coordination, and process improvement. Assists with training materials, Business Continuity Management and Policy Management releases, issue resolution, and responsible AI use-case exploration within governance, risk, and compliance processes.

Own overseas AI product operations across user research, AI experience strategy, LLM quality governance, multilingual corpus evaluation, and data-driven iteration. Research global user needs, define personalized and proactive AI experiences, analyze model failures and behavior data, establish evaluation workflows, and partner with product and algorithm teams to improve conversational quality, recommendation performance, adoption, satisfaction, and retention.

Conduct research in embodied AI, focusing on vision-language-action models. Responsibilities include problem formulation, coding, model training and evaluation, data collection pipeline development, dataset creation, documentation, and meeting strict deliverable deadlines. The role requires on-site presence and may involve robotic arms, end-effectors, teleoperation, and robotic simulation.

Designs AI-powered scientific workflows for skin and scalp biology discovery, integrating biological data, computational models, experimental validation, and agentic AI systems. The role develops DBTL workflows, enables AI agents to use scientific databases and tools, evaluates biological foundation models and AI Co-Scientist architectures, and collaborates with experimental and technology teams to create model-driven discovery capabilities.

Build AI and agent-based solutions, ML models, full-stack React and Python applications, and production-grade AI platform services. Develop MLOps and CI/CD pipelines across GCP and on-premises Kubernetes, deploy model-serving infrastructure, implement observability and security controls, and create reliable agent guardrails, evaluations, and audit trails. Collaborate with enterprise SMEs, contribute to architecture standards, communicate technical recommendations, and own solutions from design through support.

Develop AI-enabled automation workflows for NAND device characterization and engineering data analysis. Build data pipelines, machine learning models, anomaly detection tools, dashboards, AI assistants, and automated reporting solutions using large-scale semiconductor datasets. The role involves applying data science, generative AI, LLMs, and agentic AI to improve engineering productivity and decision-making while collaborating with cross-functional engineering teams.