Machine Learning Operations Engineer

Fathom.io; Dhahran Compound; On-site; SAR 60,000 - 120,000; Full time

Job summary

Join a pioneering AI/DataOps company as the first MLOps Engineer, where you'll shape the future of machine learning infrastructure. This dynamic role involves designing and maintaining ML pipelines, automating deployments, and implementing best practices in a fast-paced startup environment. Work alongside innovative teams on cutting-edge AI challenges and make a significant impact in the industry. If you're passionate about AI and eager to drive change, this is an exciting opportunity to lead and define MLOps at a forward-thinking organization.

Qualifications

• 2-7+ years in MLOps or DevOps with strong AI/ML focus.
• Hands-on with cloud platforms and container orchestration.

Responsibilities

• Design and maintain end-to-end ML pipelines and automate model deployment.
• Collaborate with teams to integrate ML workloads into the infrastructure.

Skills

MLOps

DevOps

AI/ML focus

Problem-solving

Python

Bash

Go

Tools

GCP

Kubernetes

Docker

Kubeflow

MLflow

TF Serving

ArgoCD

Terraform

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

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

Requirements

1. 2-7+ years of experience in MLOps, DevOps, or related fields with a strong AI/ML focus.
2. Hands-on experience with cloud platforms (GCP preferred) and container orchestration (Kubernetes, Docker).
3. Proficiency in AI/ML pipeline frameworks (Kubeflow, MLflow, TFX, or similar).
4. Strong knowledge of CI/CD tools (GitHub Actions, ArgoCD, or similar) for ML models.
5. Experience with monitoring AI/ML models in production.
6. Strong programming skills in Python, Bash, or Go.
7. Familiarity with model serving frameworks (TF Serving, Triton, BentoML) and decentralized/distributed computing (Ray, Spark).
8. Experience in optimizing AI/ML workloads for GPUs and CPUs.
9. Excellent problem-solving skills and ability to work in a fast-paced, evolving environment.

Nice to Have

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

Why Join Us?

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