20 meilleures offres (ai security specialist) (à pourvoir)

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- Temps plein
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CRANIUM —Zaventem
- Temps plein
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Copaco Nederland BV —Anvers
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- Anglais
- Bachelier
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- Assurance groupe
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il y a 5 jours
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NVISO —Bruxelles
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- Azure
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NTT Ltd —Diegem
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Atlas Copco Group —Wilrijk
- Azure
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- Intelligence artificielle
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TÜV AUSTRIA —Rotselaar
- Temps plein
- Informatique
- Azure
- Informatique
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Senior AI DevOps / LLMOps

TechBiz Global GmbH
Namur

Postuler rapidement

Détails de l'emploi

Temps plein

Qualifications

CI/CD
Expérience avec les centres de données
Azure
Kubernetes
Ansible
DevOps
AWS
Bachelier
Virtualisation
Terraform
GitHub
Intelligence artificielle
GitLab

Description complète du poste

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an Senior AI DevOps / LLMOps specialist to join one of our clients' teams. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.

Key Responsibilities

Automation of Build-to-Production
Design and implement robust CI/CD pipelines tailored for AI, covering model weights,

dataset versioning, and application code.

Develop specialized workflows for PromptOps, ensuring that system prompts are

version-controlled, tested for regressions, and deployed with the same rigor as traditional

code.

Automate the deployment of Agentic workflows, managing the complexities of stateful

AI interactions and multi-agent handoffs.

2. AI Infrastructure as Code (IaC)

Provision and manage high-performance compute environments (GPU clusters, TPU

pods) using Terraform, Pulumi, or Ansible.

Define and enforce Policy-as-Code for AI endpoints to ensure compliance with security,

cost-usage limits, and data residency requirements.

Maintain a consistent environment across Hybrid Infrastructure, ensuring seamless

parity between On-Premises development and Cloud production.

3. Safe Experimentation & Controlled Releases

Architect Progressive Delivery strategies for AI, including Canary releases, Blue-Green

deployments, and Shadowing (where new models run in parallel with production to

compare outputs).

Build “Evaluation-in-the-Loop” gates within the pipeline to automatically test for bias,

hallucination, and performance degradation before a release.

Implement A/B testing frameworks specifically designed for LLM outputs and agentic

behavior.

4. Monitoring & Observability

- Establish deep observability into Inference Endpoints, tracking metrics like tokens-per-

second, latency, and drift in model accuracy.

Integrate feedback loops that capture production “edge cases” to feed back into the

training and fine-tuning pipelines.

Must-Have Technical Skills:

Orchestration: Advanced Kubernetes (K8s) skills, specifically with KubeFlow, Ray, or

NVIDIA Triton.

CI/CD & IaC: Expertise in GitHub Actions/GitLab CI, and Terraform or Pulumi.
AI Tooling: Experience with Weights & Biases, MLflow, LangSmith, or Arize

Phoenix.

Hardware: Understanding of GPU virtualization, CUDA drivers, and on-premises

hardware management.

Security: Familiarity with Open Policy Agent (OPA) and secret management (Vault).

Experience:

10+ years in DevOps, SRE, or Cloud Engineering.
2+ years of hands-on experience in MLOps or LLMOps, specifically moving LLMs

from notebook to production.

Proven experience managing Hybrid Cloud environments (e.g., AWS/Azure + Private

Data Center).

Postuler rapidement

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