About the Role

We are looking for Senior MLOps & Infrastructure Engineers to build and operate the hybrid AI computing platform behind VinFast’s ADAS and autonomous driving programme – on‑premise GPU clusters running perception model training and large‑scale inference, a multi‑petabyte sensor data archive, and the platform services used daily by our engineering and annotation teams.

This is a fullstack infrastructure role. You will work across compute, storage, networking, automation and security. We do not divide the team into narrow specialists: everyone owns a part of the platform end to end – provisioning, deployment, monitoring and incident response – and is expected to grow across the whole stack over time. Work is assigned according to what the programme needs at that moment, not by a fixed job boundary.

Much of the platform runs on‑premise, including air‑gapped segments. Experience operating without a public‑cloud safety net is valued here.

What the Team Covers

You will contribute across all of the following over time. We do not expect one person to master every part on day one, but we do expect you to be willing to work in any of them.

GPU and HPC clusters running model training, large‑scale inference and annotation workloads
MLOps: model serving, pipeline orchestration, experiment and model registry
Storage at multi‑petabyte scale: tiering, lifecycle, backup and recovery
Ingestion of large volumes of recorded sensor data, with integrity verification and metadata extraction
CI/CD, GitOps, infrastructure‑as‑code, monitoring and observability
Access control, audit logging and data protection for internal and external users

Key Responsibilities

AI Compute & Serving

Operate and scale GPU clusters across training, inference and annotation workloads; manage scheduling, utilisation and capacity
Deploy and scale high‑throughput model serving; tune GPU memory and runtime performance
Manage multi‑GPU distributed training and reproducible environment sandboxing
Scale pipeline orchestration on Kubernetes for large data processing jobs

Platform Automation & Delivery

Drive GitOps‑based deployment and maintain infrastructure‑as‑code across the platform
Build and maintain CI pipelines, secure container builds and release automation
Build monitoring, logging and alerting so that failures are detected and actionable, never silent
Lead incident response and drive follow‑up actions to closure

Data Infrastructure & Security

Operate storage at multi‑petabyte scale: object storage, local storage, tiering, backup and recovery
Operate the ingestion path for large sensor data deliveries, with integrity verification and metadata extraction
Implement access control and single sign‑on across platform services, with audit logging
Apply data protection measures to sensitive content before it reaches external users

Requirements

4+ years in MLOps, DevOps, SRE or HPC platform engineering, with production ownership of AI/ML infrastructure
Kubernetes administration at production scale: Helm, ingress (Traefik or Envoy), CNI, and GitOps (Flux or ArgoCD)
GPU & HPC: SLURM, NVIDIA Container Toolkit, CUDA runtime tuning, multi‑GPU memory debugging
Strong Linux systems skills and infrastructure‑as‑code (SaltStack or Ansible)
Python and Bash for automation, including Airflow DAGs and custom operators
Object storage, and a monitoring and logging stack (Prometheus, Grafana or equivalent)
Willingness to work across the full stack – compute, storage, networking, automation and security – rather than within a single specialty
Good communication in English – technical documentation and working with international partners

Nice to Have

Air‑gapped or restricted‑network data centre operations
Familiarity with autonomous vehicle sensor datasets (nuScenes, LiDAR point clouds)
Identity and access management at enterprise scale (Azure AD / Entra ID or similar)
Data anonymisation or data‑protection compliance requirements
High‑volume telemetry stacks such as ClickHouse or Vector

Senior MLOps & Infrastructure Engineer (DevOps/MLOps)

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