Engineer/ Senior Engineer (AI Engineering), Digital Hub

Opportunity We are seeking a talented AI Engineer to join our team at the Digital Hub Programme Centre. In this role, you will work on state-of-the-art AI Engineering initiatives, contributing to the design, development and deployment of AI systems at scale, while adhering to robust governance frameworks.

Key Responsibilities • AI Engineering Roadmap Execution: Implement the AI Engineering roadmap through research, experimentation and hands-on development. • MLOps & Tooling: Design, build and automate MLOps (CI/CD/CT/CM) pipelines and tools to streamline AI workflows (e.g. support seamless model deployment and lifecycle management). Implement domain-specific ML deployment, monitoring and retraining techniques for the AI community. • Infrastructure: Develop and manage scalable infrastructure for AI development, deployment and monitoring. • Governance & Compliance: Establish governance processes for AI systems, including release criteria, testing frameworks, retraining pipelines and continuous monitoring, to ensure compliance with safety and quality standards in AI systems.

Requirements • Tertiary qualification in Computer Science, Information Systems, Computer Engineering or a related fields • Minimum of 1 year of experience in MLOps preferred • Strong programming skills with good grasp of software development best practices • Strong desire to learn and grow within the AI engineering domain • Team player with excellent communication skills • Self-motivated and driven to deliver high-quality, reliable AI solutions Required skills Machine Learning Development: • Experience with developing, deploying and scaling ML models (e.g. object detection) Software Engineering: • Proficient in Python programming • Proficient in Linux Operating Systems (E.g. Ubuntu, RHEL) • Proficient in bash and yaml • Proficient with version control (e.g. Git), • Proficient with containerisation and orchestration (e.g. Docker/Podman, Kubernetes, OpenShift Container Platform) Previous experience would be advantageous • MLOps Expertise: o Data and model versioning (E.g. DVC, clearml-datasets), model serving (e.g. vLLM, Triton Inference Server) and experiment orchestration (e.g. clearml) o Model testing (e.g. directional expectation and invariance testing, robustness testing such as adversarial AI, Brittleness and Explainability) o Model monitoring pipelines and retraining workflows (E.g. Drift Detection) • Cloud Infrastructure: o Experience with hyperconverged infrastructure (HCI), storage (e.g. S3, NFS) and networking • DevOps: o Familiarity with automation tools (e.g. Argo, Gitlab), monitoring dashboards (e.g. Prometheus/Grafana) and platforms (e.g. Kafka, Redis). o RESTful services (e.g. HTTPS, gRPC) • Programming: o Rust, Go