Machine Learning Operations Engineer

Fathom.io; Dammam; On-site; SAR 300,000 - 400,000; Full time

Job summary

A pioneering AI/DataOps company is seeking an experienced MLOps Engineer to lead the development of scalable machine learning infrastructures. As the first hire in this role, you will shape the processes for deploying and managing ML models, work on innovative projects, and collaborate with a dynamic team pushing the boundaries of AI technology.

Benefits

Opportunity to define ML infrastructure

Work on cutting-edge AI/ML challenges

Qualifications

• 2-7+ years experience in MLOps or related fields.
• Hands-on with cloud platforms, especially GCP.
• Strong programming skills in Python, Bash, or Go.

Responsibilities

• Design and maintain ML pipelines including data processing and deployment.
• Automate model deployment and lifecycle management.
• Implement CI/CD workflows for ML models.

Skills

MLOps

DevOps

AI/ML

Python

Cloud Platforms

Kubernetes

Docker

Tools

Kubeflow

MLflow

Terraform

GitHub Actions

Job description

About The Role

We are a pioneering AI/DataOps company, marking our footprint on the global stage with a presence in Saudi Arabia, Poland, and Norway. As a pre-series A startup, we are proudly backed by one of the world's leading corporations, underscoring our potential and the innovative spirit driving our mission. Our platform is engineered to address complex business challenges through cutting-edge AI solutions, and we are on the brink of launching a product set to revolutionize the industry.

Role Overview

As our first MLOps Engineer, you will play a critical role in shaping the infrastructure and processes for deploying, monitoring, and scaling machine learning models. You'll work closely with our data science, engineering, and DevOps teams to build a robust ML pipeline and ensure seamless model deployment and management.

Responsibilities

• Design, build, and maintain end-to-end ML pipelines, including data processing, model training, evaluation, and deployment.
• Automate model deployment and lifecycle management across cloud and potential on-prem environments.
• Establish CI/CD workflows for ML models, ensuring reproducibility and traceability.
• Implement monitoring, logging, and alerting for model performance and drift detection.
• Optimize ML training and inference workloads for cost and performance efficiency.
• Collaborate with DevOps and engineering teams to integrate ML workloads with broader infrastructure.
• Define and implement MLOps best practices, including experiment tracking, versioning, and governance.
• Evaluate and recommend tools and frameworks for MLOps, considering both cloud and on-prem scenarios.
  
  

Requirements

• 2-7+ years of experience in MLOps, DevOps, or related fields with a strong AI/ML focus.
• Hands-on experience with cloud platforms (GCP preferred) and container orchestration (Kubernetes, Docker).
• Proficiency in AI/ML pipeline frameworks (Kubeflow, MLflow, TFX, or similar).
• Strong knowledge of CI/CD tools (GitHub Actions, ArgoCD, or similar) for ML models.
• Experience with monitoring AI/ML models in production.
• Strong programming skills in Python, Bash, or Go.
• Familiarity with model serving frameworks (TF Serving, Triton, BentoML) and decentralized / distributed computing (Ray, Spark).
• Experience in optimizing AI/ML workloads for GPUs and CPUs.
• Excellent problem-solving skills and ability to work in a fast-paced, evolving environment.
  
  

Nice to Have

• Experience with hybrid cloud/on-prem deployments.
• Experience in infrastructure-as-code (Terraform, Pulumi).
• Prior startup experience or working in an environment with evolving ML infrastructure.
  
  

Why Join Us?

• Opportunity to be the first MLOps hire and define the future of ML infrastructure at Fathom.
• Work on cutting-edge AI/ML challenges with a team that values innovation and impact.