AI / MLOps Engineer (Infrastructure, Monitoring & Deployment)

Job Description

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Summary

This role spans infrastructure setup, orchestration, model serving, and operational reliability, and will closely support all aspects of AI model lifecycle in a production environment.

Key Responsibilities

Operate andmanage Kubernetes or OpenShift clusters for multi-node orchestration

Deploy and manage LLMs and other AI models for inference using Triton Inference Server or custom endpoints

Automate CI/CD pipelines for model packaging, serving, retraining, and rollback using GitLab CI or ArgoCD

Set up model and infrastructure monitoring systems (Prometheus, Grafana, NVIDIA DCGM)

Implement model drift detection, performance alerting, and inference logging

Manage model checkpoints, reproducibility controls, and rollback strategies

Track deployed model versions using MLFlow or equivalent registry tools

Implement secure access controls for model endpoints and data artifacts

Collaborate with AI / Data Engineer to integrate and deploy fine-tuned datasets

Ensure high availability, performance, and observability of all AI services in production

Required Qualifications

3+ years experience in DevOps, MLOps, or AI/ML infrastructure roles

10+ overall experience with solution operations

Proven experience with Kubernetes or OpenShift in production environments, preferably certified.

Familiarity with deploying and scaling PyTorch or TensorFlow models for inference

Experience with CI/CD automation tools with Open Shift / Kubernetes

Hands-on experience with model registry systems (e.g., MLFlow, KubeFlow)

Experience with monitoring tools (e.g., Prometheus, Grafana) and GPU workload optimization

Strong scripting skills (Python, Bash) and Linux system administration knowledge

Preferred (Bonus) Skills

Experience with Triton Inference Server or NVIDIA AI stack