AI Ops Engineer

Socium - Teams Done Differently; Abu Dhabi; On-site; AED 240,000 - 360,000; Full time

Job summary

Socium - Teams Done Differently is seeking an experienced AI/ML infra engineer to design and operate production-grade MLOps/LLMOps pipelines onsite in Abu Dhabi. You will deploy, monitor, and version AI models, optimize GPU workloads, and support RAG and agentic AI workflows in live environments.

You will work with Kubernetes, CI/CD, and cloud platforms to ensure scalable, reproducible AI solutions while maintaining governance and cost control.

Qualifications

• Experience in DevOps, Cloud, SRE, Platform Engineering, or Infrastructure.
• Hands-on with AI Ops, MLOps, or LLMOps.
• Experience deploying AI/ML models in production and monitoring.
• Familiarity with GPU-based inference and AI workloads.

Responsibilities

• Build and maintain end-to-end MLOps/LLMOps pipelines using DevOps best practices.
• Deploy, monitor, version, and roll back AI models in production.
• Build scalable and GPU-optimized workflows for AI training and inference.
• Operate AI models and applications as reliable production services.
• Support RAG and agentic AI workflows in production environments.
• Monitor AI inference usage, including TPM/quota, performance, and consumption/cost.
• Implement data preprocessing, feature engineering, and model evaluation workflows as required.
• Support fine-tuning and deployment of pre-trained LLM, NLP, and computer-vision models.
• Ensure AI solutions are reproducible, governed, documented, and compliant.
• Contribute to AI infrastructure strategy and responsible/ethical AI practices.

Skills

DevOps

SRE

Platform engineering

Infrastructure

MLOps

LLMOps

RAG workflows

AI workflows

Kubernetes

CI/CD

Containers

Cloud platforms

Tools

Kubernetes

CI/CD

Docker

Cloud environments

Terraform

Job description

Location: Abu Dhabi (Onsite)

Contract Duration: Initial 12 months extendable contract

Key Responsibilities

• Build and maintain end-to-end MLOps/LLMOps pipelines using DevOps best practices.
• Deploy, monitor, version, and roll back AI/ML models in production.
• Build scalable and GPU-optimized workflows for AI training and inference.
• Operate AI models and applications as reliable production services.
• Support RAG and agentic AI workflows in production environments.
• Monitor AI inference usage, including TPM/quota, performance, and consumption/cost.
• Implement data preprocessing, feature engineering, and model evaluation workflows as required.
• Support fine-tuning and deployment of pre-trained LLM, NLP, and computer-vision models.
• Ensure AI solutions are reproducible, governed, documented, and compliant.
• Contribute to AI infrastructure strategy and responsible/ethical AI practices.

Requirements

• Experience in DevOps, Cloud, SRE, Platform Engineering, or Infrastructure.
• Experience transitioning into or working with AI Ops, MLOps, or LLMOps.
• Hands-on experience with Kubernetes, CI/CD, containers, and cloud environments.
• Experience deploying and operating AI/ML models in production.
• Familiarity with GPU-based inference and AI/ML workloads.
• Understanding of model monitoring, versioning, deployment, and rollback.
• Familiarity with LLMs, RAG, agentic workflows, or other production AI applications.
• Strong automation and troubleshooting skills.