AI Jobs in Singapore

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The AI Deployment Engineer will serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with their engineering teams to build coding workflows. They will partner directly with customers to design and implement AI-enhanced development workflows from rapid prototyping through scalable production rollout. Responsibilities also include building high-quality demos, reference implementations, and workflow automations using Codex as part of the development process. The engineer will lead large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. They will contribute technical content such as examples, guides, patterns, and best practices to the OpenAI Cookbook to assist the broader developer community. Additionally, the role involves gathering high-fidelity product insights from customer deployments and translating them into product proposals and model feedback for internal teams. The engineer will influence customer strategy and decision-making regarding the integration of AI coding tools into their software development lifecycle, technical roadmap, and workflows. Finally, they will act as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

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Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team’s capabilities. Rapidly prototype proof-of-concepts for customer use cases. Work across Engineering, Product, and Solutions teams to unblock customers and push the boundaries of AI adoption.

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As a Robotics Application Engineer specializing in Intelligent Manufacturing Automation, the responsibilities include closely collaborating with the automation group and industry partners to advance manufacturing automation by extending capabilities and solutions. Daily tasks involve integrating and developing robotics solutions, including processes, software features, and integrating state-of-the-art AI for manufacturing automation. The role includes working with the team leading the deployment of impactful robotic systems in production. Additional responsibilities entail contributing to the technical development and integration of advanced robotic automation solutions for manufacturing automation using the Intrinsic platform, ROS, and AI capabilities, collaborating with research and industry partners to integrate AI and automation into factory settings, and documenting designs, processes, and results while communicating effectively with internal technical teams and partners.

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The role involves defining the internal AI roadmap by partnering with Security, Legal, and business leaders, operating the enterprise AI stack including LLMs, vector databases, and gateways, enforcing consistent patterns for tool calling, prompt versioning, state management, and error handling to prevent fragmented agent implementations, and managing the full model lifecycle from evaluation and testing to upgrades and deprecations. The position includes proactively interviewing teams like Talent, Support, and Sales to identify manual workflows for automation with agentic AI, independently building and deploying proof of concepts to demonstrate ROI before scaling, owning the token procurement process, building forecasting and chargeback models to control spend, creating dashboards to track SLAs and system performance, and identifying opportunities for cost-saving and performance tuning.

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Forward Deployed Engineers lead complex end-to-end deployments of frontier models in production alongside strategic customers, owning discovery, technical scoping, system design, build, and production rollout. They operate across multiple deployments from prototype to stable production, build full-stack systems to deliver customer value, embed closely with customer teams to understand needs, guide adoption, scope work, sequence delivery, remove blockers, make trade-offs between scope, speed, and quality, contribute directly in code when necessary, codify working patterns into reusable tools and playbooks, share field feedback to help Research and Product teams improve models, and keep teams moving with clarity and follow-through.

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Design and deliver production-grade AI agents that are central to Sierra's growth, ensuring they are highly performant, reliable, and scalable in production environments across industries such as finance, healthcare, and commerce. Drive the Agent Development Life Cycle (ADLC) with complete ownership and autonomy from initial pilot through deployment and continuous iteration, building, tuning, and evolving AI agents in production and defining ADLC best practices. Partner directly with leaders at large enterprises and cutting-edge startups to understand their business challenges and build AI agents that transform operations at scale. Build the future of Sierra's platform by gathering customer feedback, prototyping new tools and features, and collaborating with research, product, and platform teams to enhance AI agent development and Sierra's product. Work on projects including designing AI agents to manage subscription churn, developing agents for complex customer interactions, creating industry-specific AI agent frameworks, facilitating design partnerships for new product initiatives, and experimenting with latest voice models for enterprise-grade integration.

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth. Take complete ownership of the Agent Development Life Cycle (ADLC) from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices. Partner directly with leaders of large enterprises and cutting-edge startups to understand business challenges and build AI agents that transform operations at scale. Work with customers to guide the evolution of Sierra's core platform by surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape the future of AI agent development and Sierra's product.

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, running in production environments across industries like finance, healthcare, and commerce. Drive the Agent Development Life Cycle (ADLC) with complete ownership and autonomy from pilot through deployment and continuous iteration, building, tuning, and evolving AI agents in production and defining ADLC best practices. Partner with large enterprises and startups to understand business challenges and build AI agents that transform operations at scale. Build the future of Sierra's platform by surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and product evolution.

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As an Applied AI Engineer, you will be responsible for building and shipping AI features end-to-end, from model to system to user experience. This includes designing and iterating on prompts, tools, memory, and agent workflows, turning raw model outputs into structured and predictable behaviors, debugging issues across the full stack including model, orchestration, infrastructure, and user experience, optimizing for latency, cost, and production reliability, developing lightweight evaluation frameworks to measure real-world performance, and working closely with product and engineering teams to translate ambiguous problems into working systems. You will own problems end-to-end from shaping model behavior to building the surrounding systems to ensure reliable production performance.