AI Engineer

Univers; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Univers's Global Impact AI Lab in Singapore seeks an AI Engineer to design and deploy scalable AI systems that bridge advanced research and real‑world production across enterprise and industrial domains.

You will build end‑to‑end training, evaluation, and inference pipelines, develop agentic frameworks, and optimize latency and throughput while collaborating with Applied AI, AIoT, product, and engineering teams to ship robust platform components.

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.

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.

Skills

Python

PyTorch

ML pipelines

MLOps

System design

Collaboration

Education

Bachelor's/Master's/PhD in CS or related field

Tools

Docker

Kubernetes

Job description

Are you looking to build intelligent systems that move beyond digital experiments and into the physical world? The Global Impact AI Lab at Univers was established to accelerate the next generation of enterprise AI and intelligent IoT innovation — transforming how industries operate, optimize resources, and build resilient infrastructure at scale.

Our mission is to translate advanced AI research into production‑grade systems that enhance energy efficiency, operational performance, and sustainability across critical sectors. We focus on applied intelligence — where models and agentic systems connect directly with real‑world data streams, operate in live environments, and continuously improve through operational feedback. AI is moving from promise to accountability.

Enterprises expect systems that deliver measurable outcomes in complex, high‑stakes physical environments. The Global Impact AI Lab is building the intelligence layer that turns multimodal physical signals into enterprise‑critical intelligence and action. If you are motivated by engineering scalable AI systems and enabling advanced models to operate reliably in real‑world environments, this is an opportunity to translate innovation into measurable impact.

For more information, please visit https://univers.com/

Role Overview

As an AI Engineer at the Global Impact AI Lab, 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.

You will work closely with Applied AI and AIoT Scientists to transition validated prototypes into robust, scalable systems, collaborating with product and engineering teams to ensure smooth integration into production environments.

Operating in both 0→1 and scaling environments, you will build and refine model training and inference pipelines, develop agentic frameworks that coordinate reasoning and workflow execution, and architect AI systems that meet real‑world requirements for latency, throughput, scalability, and runtime efficiency.

You will play 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.

This role requires strong systems thinking, engineering discipline, and the ability to translate advanced AI capabilities into deployable, high‑performance systems.

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