Senior AI DevOps / LLMOps

Krakow, Podlaskie, Poland; Krosno, Dolnośląskie, Poland; Lublin, Lubelskie, Poland; Poland, Mazowieckie, Poland; Poznan, Wielkopolskie, Poland; Warsaw, Warmińsko-Mazurskie, Poland; Pilsen, Plzeňský kraj, Czechia; Praha, Praha, Hlavní město, Czechia; Riga, Rīga, Latvia; Vilnius, Vilniaus apskritis, Lithuania; Athens, Attikí, Greece; Crete, Kríti, Greece; Budapest, Budapest, Hungary; Miskolc, Borsod-Abaúj-Zemplén, Hungary; Brussels, Brussels, Belgium; Liège, Walloon Region, Belgium; Mons, Walloon Region, Belgium; Namur, Walloon Region, Belgium; Sofia, Sofia, Bulgaria; Bordeaux, Nouvelle-Aquitaine, France; Grenoble, Auvergne-Rhône-Alpes, France; Lille, Hauts-de-France, France; Lyon, Auvergne-Rhône-Alpes, France; Montpellier, Occitanie, France; Nantes, Pays-de-la-Loire, France; Paris, Île-de-France, France; Toulouse, Occitanie, France; Baden-Baden, Baden-Württemberg, Germany; Bad Homburg, Hessen, Germany; Augsburg, Bayern, Germany; Aachen, Nordrhein-Westfalen, Germany; Bergisch Gladbach, Nordrhein-Westfalen, Germany; Berlin, Berlin, Germany; Bernau, Brandenburg, Germany; Bielefeld, Nordrhein-Westfalen, Germany; Bramsche, Niedersachsen, Germany; Chemnitz, Sachsen, Germany; Darmstadt, Hessen, Germany; Darmstadt, Hessen, Germany; Dresden, Sachsen, Germany; Dresden, Sachsen, Germany; Dusseldorf, Nordrhein-Westfalen, Germany; Düsseldorf, Rheinland-Pfalz, Germany; Essen, Nordrhein-Westfalen, Germany; Frankfurt, Hessen, Germany; Freiburg, Baden-Württemberg, Germany; Geoergsmarienhütte, Niedersachsen, Germany; Görlizt, Sachsen, Germany; Göttingen, Niedersachsen, Germany; Greifswald, Mecklenburg-Vorpommern, Germany; Halle, Sachsen-Anhalt, Germany; Hamburg, Hamburg, Germany; Hannover, Niedersachsen, Germany; Heinsberg, Nordrhein-Westfalen, Germany; Karlsruhe, Baden-Württemberg, Germany; Kiel, Schleswig-Holstein, Germany; Koln, Nordrhein-Westfalen, Germany; Leipzig, Sachsen, Germany; Leverkusen, Nordrhein-Westfalen, Germany; Lingen, Niedersachsen, Germany; Magdeburg, Sachsen-Anhalt, Germany; Mainz, Rheinland-Pfalz, Germany; Mittenwalde, Brandenburg, Germany; Munich, Bayern, Germany; Münster, Nordrhein-Westfalen, Germany; Münster, Nordrhein-Westfalen, Germany; Neu-Ulm, Bayern, Germany; Neuruppin, Brandenburg, Germany; Neuss, Nordrhein-Westfalen, Germany; Nuremberg, Bayern, Germany; Osnabrück, Niedersachsen, Germany; Paderborn, Nordrhein-Westfalen, Germany; Potsdam, Brandenburg, Germany; Quedlinburg, Sachsen-Anhalt, Germany; Rimbach, Hessen, Germany; Rostock, Mecklenburg-Vorpommern, Germany; Schwerin, Mecklenburg-Vorpommern, Germany; Stuttgart, Baden-Württemberg, Germany; Ulm, Baden-Württemberg, Germany; Varel, Niedersachsen, Germany; Viechtach, Bayern, Germany; Warendorf, Nordrhein-Westfalen, Germany; Wiesbaden, Hessen, Germany; Wuppertal, Nordrhein-Westfalen, Germany; Lisbon, Lisboa, Portugal; Porto, Lisboa, Portugal; Zagreb, Zagrebačka županija, Croatia; Zagreb, Grad Zagreb, Croatia; Sarajevo, Federacija Bosne i Hercegovine, Bosnia and HerzegovinaRemote · Europe

Senior · fulltime_permanent · Engineering

POSTED 19 JUNVERIFIED 21H AGO

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.

Requirements

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).