Senior Staff Engineer (DevOps Engineer- AI / ML)

Nagarro; Abu Dhabi; Remote; AED 120,000 - 200,000; Full time

Job summary

A leading tech firm in the UAE seeks a DevOps / MLOps Engineer to develop automated infrastructure for LLM-powered GenAI. Responsibilities include designing CI/CD pipelines and managing MLOps workflows. Ideal candidates will have strong skills in Docker, Kubernetes, and scripting. This full-time position offers remote work options.

Qualifications

• Hands-on experience with CI/CD tools like Jenkins or GitHub Actions.
• Proficiency with Terraform, Kubernetes, and cloud-native practices.
• Strong scripting skills in Bash and Python.
• Experience with ML model tracking and deployment using MLflow.

Responsibilities

• Design and maintain CI/CD pipelines.
• Automate infrastructure provisioning using Terraform.
• Implement and monitor MLOps workflows.
• Support deployment of LLM-based models in production.

Skills

Jenkins

GitHub Actions

Terraform

Kubernetes

Docker

Bash scripting

Python scripting

MLflow

Job description

Job Description

Looking for a DevOps / MLOps Engineer to build and manage scalable automated infrastructure for our LLM‑powered GenAI platform. You’ll enable fast iteration and reliable deployment of models and services through robust CI / CD pipelines, container orchestration, and ML lifecycle tooling.

Key Responsibilities

• Design and maintain CI / CD pipelines using Jenkins, GitHub Actions or similar.
• Automate infrastructure provisioning using Terraform and manage services with Kubernetes.
• Write and maintain Bash / Python scripts for automation and operational tooling.
• Implement and monitor MLOps workflows using tools like MLflow, Azure ML or similar.
• Support deployment and monitoring of LLM‑based models and APIs in production.

Required Skills

• Hands‑on experience with Jenkins, GitHub Actions or equivalent CI / CD tools.
• Proficiency with Terraform, Kubernetes, Docker and cloud‑native practices.
• Strong scripting skills in Bash and Python.
• Experience with ML model tracking, versioning and deployment using MLflow or similar.
• Familiarity with cloud platforms (e.g., Azure, AWS or GCP).

Nice to Have

• Exposure to LLM / GenAI deployment workflows.
• Experience with model performance monitoring and observability tools (Prometheus, Grafana, etc.).
• Security and cost optimization best practices for ML infrastructure.

Remote Work

Yes

Employment Type

Full‑time

Key Skills

Computer Science, Docker, Kubernetes, Python, VMware, C / C++, Go, System Architecture, gRPC, OS Kernels, Perl, Distributed Systems

Department / Functional Area

Engineering

Experience

years

Vacancy

1