Senior AI DevOps / LLMOps Specialist

TechBiz Global provides top-tier recruitment services to our esteemed clients. We are currently seeking a Senior AI DevOps / LLMOps specialist to join the innovative team of one of our clients. If you are looking for an exciting opportunity to grow within an 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.

1. 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.

1. 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.

1. 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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