We are looking for an experienced AI Infrastructure Architect for a banking client. The role is to design, build, and optimise the next‑generation platforms that power our enterprise AI and machine learning ecosystem. This is a strategic, high‑impact role for a technical leader who can bridge architecture, engineering, and operations to deliver scalable, secure, and high‑performance AI infrastructure.

Key Responsibilities

- Design and architect end‑to‑end AI infrastructure, including compute, storage, networking, and orchestration layers for large‑scale ML workloads.
  - Lead the implementation of GPU‑accelerated platforms for training, inference, and model lifecycle management.
  - Develop cloud‑native and hybrid AI architectures leveraging Kubernetes, containerisation, and distributed systems.
  - Collaborate with data science and engineering teams to ensure infrastructure meets performance, scalability, and reliability requirements.
  - Establish best practices for MLOps, observability, and automation across the AI/ML pipeline.
  - Ensure compliance, security, and governance for AI workloads in a regulated environment.
  - Evaluate emerging AI technologies and drive adoption of modern tooling, frameworks, and accelerators.

Requirements

Strong experience in AI/ML infrastructure architecture across cloud or on‑prem environments.
Experience working on big data infrastructure including Hadoop, Hive, Spark, etc.
Hands‑on expertise with Kubernetes, Docker, and distributed compute.
Deep understanding of GPU platforms (NVIDIA, CUDA, NCCL, Triton, etc.).
Experience with MLOps tooling such as MLflow, Kubeflow, Airflow, or similar.
Knowledge of data platforms and pipelines supporting large‑scale AI workloads.

Interested candidates please email your latest resume to subagio@tangspac.com

Infrastructure Architect (ai Or Big Data)

Job description