# DataOps Engineer (AI Platform Engineer) at Exness jobs for internal candidates

> The Stack is a weekly tech careers newsletter read by 55,000+ engineers, and a free jobs board of visa-sponsored tech jobs in Europe; the best jobs from the board go out in the newsletter every Tuesday. This is one job from the board.

> Responsibilities: - Design, deploy and operate an on-prem AI

- **Company:** Exness jobs for internal candidates (https://jobs.trythestack.co/companies/exness-jobs-for-internal-candidates)
- **Location:** Cyprus
- **Work arrangement:** On-site
- **Employment type:** Full-time
- **Experience level:** 5–10 years
- **Visa sponsorship:** Offered, according to the listing
- **Key skills:** Kubernetes, GPU Infrastructure, Python, Go, Distributed Systems, Model Serving, CI/CD, Observability, Linux, vLLM, Nvidia Triton, KServe, MLflow, RDMA, Performance Testing, GPU Scheduling
- **Posted by employer:** 2026-05-15
- **Added to The Stack:** 2026-07-13
- **Status:** May no longer be active (posted more than 60 days ago)
- **Apply:** https://job-boards.eu.greenhouse.io/internalhiring/jobs/4829611101 (the employer's own application page)
- **Human page:** https://jobs.trythestack.co/jobs/dataops-engineer-ai-platform-engineer-exness-jobs-for-internal-candidates

## About the role

Responsibilities:
- Design, deploy and operate an on-prem AI platform for multi-node GPU clusters and Kubernetes.
- Collaborate on GPU server selection, high-performance networking and RDMA-enabled cluster configuration.
- Manage GPU partitioning/MIG, scheduling and runtime integration to maximise utilization and reliability.
- Deploy and maintain model serving runtimes (vLLM, ONNX, SGLang, Nvidia Triton, KServe) and benchmarking setups.
- Build CI/CD pipelines and tooling for model packaging, versioning, deployment and reproducible delivery.
- Implement model lifecycle tooling: experiment tracking, registries and support for fine-tuning (e.g., LoRA).
- Create observability and tracing for GPU workloads and inference flows (utilisation, memory, throughput, latency).
- Run performance tests and benchmarks across runtimes and hardware; identify and resolve bottlenecks.
- Ensure platform security and compliance for model serving and data handling.

Requirements:
- Bachelor’s or Master’s in CS, Engineering or related field and 5+ years in infrastructure/platform or distributed systems.
- Strong Kubernetes experience running production workloads and Linux proficiency.
- Programming skills in Python and/or Go; experience building automation and CI/CD for ML workloads.
- Hands-on experience with GPU infrastructure (NVIDIA/AMD), multi-GPU clusters and MIG configurations.
- Familiarity with model serving runtimes (Triton, KServe, vLLM, ONNX) and model lifecycle tools (MLflow).
- Understanding of high-performance networking, RDMA, GPU scheduling and performance benchmarking.
- Good troubleshooting, observability and system optimisation skills.

## Responsibilities

Design and operate an on-prem AI platform for deploying and scaling models across multi-node GPU clusters and Kubernetes. Build reliable infrastructure for large-scale model inference, including CI/CD pipelines and observability tools for GPU workloads.

## Requirements

Requires a degree in Computer Science or Engineering with 5+ years of experience in platform engineering or distributed systems. Proficiency in Kubernetes, Python/Go, and experience with GPU infrastructure and model serving runtimes is essential.

## FAQ

- **Is this DataOps Engineer (AI Platform Engineer) role still open?** It was posted on 2026-05-15. Listings more than 30 days old may no longer be active, so check Exness jobs for internal candidates's site before applying.
- **Does Exness jobs for internal candidates offer visa sponsorship?** This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Exness jobs for internal candidates's site.
- **Where is this role located?** The listing gives the location as Cyprus. It is marked on-site.
- **What skills does this role ask for?** The listing names: Kubernetes, GPU Infrastructure, Python, Go, Distributed Systems, Model Serving, CI/CD, Observability, Linux, vLLM, Nvidia Triton, KServe, MLflow, RDMA, Performance Testing, GPU Scheduling.

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## Notes for agents

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