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
remoteroles summary, based on the employer's posting.
What you'll do
- Build AI-focused delivery pipelines that track model weights, datasets and application code through production
- Version, test and release system prompts, then automate deployment of agent workflows and multi-agent handoffs
- Provision GPU or TPU environments and apply policies for endpoint security, spending limits and data residency
- Use staged releases and evaluation gates to compare models and catch bias, hallucinations or performance drops
- Monitor inference speed, latency and accuracy drift, feeding unusual production cases into training workflows
What you bring
- At least ten years in DevOps, SRE or cloud engineering, plus two years putting LLMs into production
- Advanced Kubernetes skills with KubeFlow, Ray or another listed orchestration tool
- Expertise in GitHub Actions or GitLab CI, and Terraform or Pulumi
- Hands-on work with an AI tooling option such as Weights & Biases or MLflow, plus GPU and CUDA knowledge
- Experience with hybrid cloud environments and familiarity with OPA or Vault
Who this fits
This senior role suits someone who has moved LLMs from notebooks into production and managed hybrid cloud infrastructure. It is a remote full-time position 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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