Information Technology & Services

Senior AI DevOps / LLMOps

RemoteRemoteFull TimeSeniorPosted 1 month agoHidden Gem · Himalayas

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Required skills

LLMOps
PromptOps
Agentic workflows
Kubernetes
KubeFlow
Ray
NVIDIA Triton
GitHub Actions
GitLab CI
Terraform

Nice to have

Ansible

Role summary

TechBiz Global is seeking a Senior AI DevOps / LLMOps Specialist for a client role. This position focuses on automating AI/ML pipelines, managing AI infrastructure as code, implementing safe experimentation and controlled releases for AI models, and establishing deep observability into inference endpoints. The role requires expertise in Kubernetes, CI/CD, IaC, AI tooling, GPU hardware, and security policies within hybrid cloud environments. Candidates should have 10+ years in DevOps/SRE/Cloud Engineering and 2+ years specifically in MLOps/LLMOps.

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an Senior AI DevOps / LLMOpsspecialist 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.

Requirements

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).

Highlights

full time and remote job

- fluent English is needed

Originally posted on Himalayas

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