Senior AI DevOps / LLMOps

Location: Krakow, Poland; Krosno, Poland; Lublin, Poland; Poland; Poznan, Poland; Warsaw, Poland; Pilsen, Czechia; Praha, Czechia; Riga, Latvia; Vilnius, Lithuania; Athens, Greece; Crete, Greece; Budapest, Hungary; Miskolc, Hungary; Brussels, Belgium; Liège, Belgium; Mons, Belgium; Namur, Belgium; Sofia, Bulgaria; Bordeaux, France; Grenoble, France; Lille, France; Lyon, France; Montpellier, France; Nantes, France; Paris, France; Toulouse, France; Baden-Baden, Germany; Bad Homburg, Germany; Augsburg, Germany; Aachen, Germany; Bergisch Gladbach, Germany; Berlin, Germany; Bernau, Germany; Bielefeld, Germany; Bramsche, Germany; Chemnitz, Germany; Darmstadt, Germany; Dresden, Germany; Dusseldorf, Germany; Düsseldorf, Germany; Essen, Germany; Frankfurt, Germany; Freiburg, Germany; Geoergsmarienhütte, Germany; Görlizt, Germany; Göttingen, Germany; Greifswald, Germany; Halle, Germany; Hamburg, Germany; Hannover, Germany; Heinsberg, Germany; Karlsruhe, Germany; Kiel, Germany; Koln, Germany; Leipzig, Germany; Leverkusen, Germany; Lingen, Germany; Magdeburg, Germany; Mainz, Germany; Mittenwalde, Germany; Munich, Germany; Münster, Germany; Neu-Ulm, Germany; Neuruppin, Germany; Neuss, Germany; Nuremberg, Germany; Osnabrück, Germany; Paderborn, Germany; Potsdam, Germany; Quedlinburg, Germany; Rimbach, Germany; Rostock, Germany; Schwerin, Germany; Stuttgart, Germany; Ulm, Germany; Varel, Germany; Viechtach, Germany; Warendorf, Germany; Wiesbaden, Germany; Wuppertal, Germany; Lisbon, Portugal; Porto, Portugal; Zagreb, Croatia; Sarajevo, Bosnia and Herzegovina Workplace: remote 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).