Senior AI DevOps / LLMOps

• 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

• 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

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.