Senior AI DevOps / LLMOps

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

1. 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.