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

• 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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