At the company, 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.
- AI Infrastructure as Code (IaC): Provision and manage high-performance compute environments (GPU clusters, TPU pods) using Terraform, Pulumi, or Ansible.
- AI Infrastructure as Code (IaC): Define and enforce Policy-as-Code for AI endpoints to ensure compliance with security, cost-usage limits, and data residency requirements.
- AI Infrastructure as Code (IaC): Maintain a consistent environment across Hybrid Infrastructure, ensuring seamless parity between On-Premises development and Cloud production.
- 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).
- Safe Experimentation & Controlled Releases: Build “Evaluation-in-the-Loop” gates within the pipeline to automatically test for bias, hallucination, and performance degradation before a release.
- Safe Experimentation & Controlled Releases: Implement A/B testing frameworks specifically designed for LLM outputs and agentic behavior.
- Monitoring & Observability: Establish deep observability into Inference Endpoints, tracking metrics like tokens-per-second, latency, and drift in model accuracy.
- Monitoring & Observability: 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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