Senior AI DevOps / LLMOps
At a glance
Senior DevOps & Release role at TechBiz Global GmbH. Remote, Poland · full-time.
Pay not stated DevOps & Release salary
devopsroles summary, based on the employer's posting.
What you'll do
- Build CI/CD workflows that version model weights, datasets and application code for AI releases
- Put prompts under version control, test them for regressions and automate deployment of multi-agent workflows
- Provision GPU or TPU environments and keep on-premises development aligned with cloud production
- Stage model rollouts with canary, blue-green or shadow releases, using evaluation checks for bias and performance
- Monitor inference speed, latency and accuracy drift, then route production edge cases into training workflows
What you bring
- At least ten years in DevOps, SRE or cloud engineering
- Two or more years taking LLMs from notebooks into production through MLOps or LLMOps work
- Advanced Kubernetes experience with KubeFlow, Ray or NVIDIA Triton
- Hands-on expertise with GitHub Actions or GitLab CI, plus Terraform or Pulumi
- Experience operating hybrid cloud environments, along with knowledge of GPU infrastructure and AI tooling
Who this fits
This senior role suits an experienced DevOps or cloud engineer who has moved LLMs into production and managed hybrid infrastructure. The role is remote and based in Krakow, Poland.
From the employer
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).
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