AI Engineer - Enterprise IoT Platform

Our client is a Singapore-headquartered industrial AI software company that sells a platform for monitoring, optimising and automating energy and physical infrastructure. The business focuses on connecting physical assets (wind and solar farms, batteries, buildings, HVAC, EV chargers, factories) to one data platform, then sells AI applications that cut energy use, cost and carbon. It serves four sectors: energy and utilities, built environment, transportation and logistics, and industrial manufacturing. Current estimated market valuation to be between USD 500 million to USD 1 billion.

They are now looking to hire AI Engineers within their newly set up Global Impact AI Lab, based in Singapore. You will design and develop scalable AI systems that bridge advanced AI research and real-world deployment across enterprise and industrial verticals. This role sits at the intersection of applied AI research, system architecture, and production integration. Therefore, playing a critical role in defining how AI systems are engineered so they can move from lab validation to production adoption — ensuring performance, reliability, and maintainability across cloud, hybrid, and edge deployments.

Key Responsibilities

• Partner with Applied AI and AIoT Scientists to transition prototypes into scalable, production-aligned architectures
• Design and implement end-to-end training, evaluation, and inference pipelines
• Develop and refine agentic frameworks that orchestrate model reasoning, tool integration, and workflow execution
• Optimize foundation model inference performance using techniques such as KV caching, batching strategies, quantization, and optimized runtimes (e.g., vLLM or similar frameworks)
• Define and architect systems that meet real-world constraints, including latency, throughput, scalability, and runtime efficiency
• Establish best practices for AI infrastructure, including containerization (Docker), orchestration (Kubernetes), and model lifecycle management
• Collaborate with product and engineering teams to integrate validated AI capabilities into scalable platform components
• Engage with technical stakeholders to ensure system performance and deployment readiness

Qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or a related field (or equivalent industry experience)
• Strong proficiency in Python and experience with PyTorch for model development and experimentation
• Experience building and optimizing training and inference pipelines for machine learning systems
• Familiarity with MLOps and model lifecycle management practices
• Experience working with containerization and orchestration technologies such as Docker and Kubernetes
• Experience optimizing foundation model inference performance and understanding runtime efficiency trade-offs
• Strong understanding of system-level considerations, including latency, throughput, scalability, and reliability
• Ability to collaborate effectively across research, product, and engineering teams
• Excellent communication skills Success in this role will be defined by your ability to transform advanced AI prototypes into scalable, high-performance systems — enabling seamless transition from innovation to production while strengthening the Global Impact AI Lab’s engineering foundation