AI Engineer - Remote

Overview

At Toku, we create bespoke cloud communications and customer engagement solutions to reimagine customer experiences for enterprises. We provide an end-to-end approach to help businesses overcome the complexity of digital transformation and deliver mission-critical CX through cloud communication solutions. Toku combines local strategic consulting expertise, bespoke technology, regional in-country infrastructure, connectivity, and global reach to serve the diverse needs of enterprises operating at scale. Headquartered in Singapore, Toku supports customers across APAC and beyond, with a growing footprint across global markets.

In Short

• Design, improve, and operate MLOps pipelines for training, deploying, and managing ML models in production.
• Build and maintain CI/CD-style workflows for model packaging, versioning, and deployment across environments.
• Operate and optimise AWS-based infrastructure for AI workloads, including compute, storage, and networking components.
• Manage GPU-enabled workloads, addressing scalability, reliability, and cost-efficiency for high-load AI applications.
• Implement monitoring and alerting for deployed models, focusing on system health, performance, and operational stability.
• Own and evolve shared tooling such as MLflow, Docker-based workflows, and deployment frameworks to improve developer productivity.
• Work closely with infrastructure, SRE, and engineering teams to align AI platform practices with broader system standards.
• Support live AI services by diagnosing deployment, scaling, and infrastructure-related issues impacting AI features.
• Ensure reproducibility, traceability, and governance across the full ML lifecycle, from experimentation to production.

Requirements

• Hands-on experience building and operating MLOps pipelines for production ML systems.
• Strong experience with AWS services used for AI workloads, including EC2, ECS, and SageMaker.
• Practical experience with Docker and container-based deployment of ML workloads.
• Experience with MLflow or similar tools for experiment tracking, model versioning, and lifecycle management.
• Experience managing GPU-based workloads and addressing performance and cost challenges at scale.
• Strong understanding of cloud infrastructure concepts as they apply to ML systems.
• Ability to work with Python-based ML codebases to support deployment and lifecycle needs.
• Working familiarity with LLMs, NLP models, and applied ML concepts sufficient to support deployment and monitoring.
• Proven experience supporting live, production ML systems with real customer impact.
• Ability to work cross-functionally with applied AI engineers, backend engineers, and infra teams.

Benefits

• Training and Development
• Discretionary Yearly Bonus & Salary Review
• Healthcare Coverage based on location
• 20 days Paid Annual Leave (excluding Bank holidays)