MLOps Engineer

Fathom.io; Saudi Arabia; On-site; SAR 240,000 - 480,000; Full time

Job summary

Fathom.io is seeking a mid-to-senior MLOps engineer to help build the intelligence layer of our AI platform. You will design infrastructure for model deployment, training, notebooks, functions, and RAG pipelines, enabling self-service workflows.

You will work at the intersection of platform engineering, ML infrastructure, distributed systems, and developer experience, delivering scalable, secure solutions for GPU-heavy workloads and edge deployments.

Qualifications

• Must have strong experience in MLOps, AI platform engineering, ML infrastructure, or distributed systems.
• Hands-on Kubernetes experience with stateful, training, notebook, serverless, or GPU workloads.
• Experience building ML platforms supporting training, experiments, notebooks, feature/data workflows, model registries, and production serving.
• Familiarity with model serving frameworks such as KServe, vLLM, Triton, or Ray Serve.
• Experience with ML lifecycle tooling, experiment tracking, model registries, and CI/CD or GitOps for ML systems.
• Experience optimizing training and inference workloads for latency, throughput, and cost.
• Understanding of GPU scheduling, quantization, batching, autoscaling, and multi-model serving.

Responsibilities

• Design and build the infrastructure powering our Intelligence layer.
• Enable automated workflows for training, deployment, lifecycle management, and inference.
• Build scalable foundations for users to create AI agents and RAG pipelines through the platform UI.
• Develop capabilities for notebooks, functions, experiments, training jobs, and model registries.
• Improve model serving, observability, versioning, evaluation, promotion, and rollback capabilities.
• Optimize GPU inference and training deployments for performance and cost.
• Explore deployment of models across centralized GPUs and edge devices.
• Automate workflows to reduce manual MLOps effort with safe, self-service features.
• Collaborate with backend, product, and AI teams to turn infrastructure into platform features.
• Help define security, multi-tenancy, resource isolation, and model governance standards.

Skills

MLOps

AI platform engineering

Distributed systems

Kubernetes

Platform design

Product-minded

Self-service UX

Tools

Kubernetes

Knative

KServe

vLLM

MLflow

LangFuse

Job description

About The Role

We're looking for a mid-to-senior MLOps engineer to help build the intelligence layer of our AI platform. This is not a traditional MLOps role focused only on maintaining pipelines and deployments. You will help create the underlying infrastructure that makes sophisticated AI workflows accessible through a simple, self-service product experience: model deployment, training, notebooks, functions, UI-driven agent creation, and RAG pipelines.

You'll work at the intersection of platform engineering, machine learning infrastructure, distributed systems, and developer experience.

What You'll Do

• Design and build the infrastructure powering our Intelligence layer.
• Enable reliable, automated workflows for model training, deployment, lifecycle management, and inference.
• Build scalable foundations for users to create, configure, and operate AI agents and RAG pipelines through the platform UI.
• Develop the platform capabilities behind managed notebooks, functions, experiments, training jobs, model registries, and serving endpoints.
• Improve model serving, observability, versioning, evaluation, promotion, and rollback capabilities.
• Optimize GPU inference and training deployments for performance, reliability, and cost efficiency.
• Explore efficient approaches for deploying models across centralized GPU infrastructure and edge devices.
• Automate workflows that otherwise require manual MLOps effort, with a focus on safe, self-service capabilities for platform users.
• Partner with backend, product, and AI teams to turn complex infrastructure into intuitive platform features.
• Help define standards for security, multi-tenancy, resource isolation, model governance, and operational reliability.

Our current stack

You'll Work With Technologies Including

• Kubernetes and Knative
• KServe and vLLM
• MLflow
• LangFuse
• GPU infrastructure and model-serving workloads
• RAG architectures, vector retrieval, agent workflows, and LLM applications

Our intelligence backend is built in Rust, so Rust experience is a strong advantage.

What We're Looking For

• Strong experience in MLOps, AI platform engineering, machine learning infrastructure, or distributed systems.
• Hands-on Kubernetes experience, including deploying and operating stateful, training, notebook, serverless, or GPU-intensive workloads.
• Experience building ML platforms that support some combination of training, experimentation, notebooks, feature/data workflows, model registries, and production serving.
• Experience with model serving frameworks such as KServe, vLLM, Triton, Ray Serve, or similar.
• Familiarity with ML lifecycle tooling such as MLflow, experiment tracking, model registries, and CI/CD or GitOps for ML systems.
• Practical experience optimizing training and inference workloads for latency, throughput, availability, and cost.
• Understanding of GPU scheduling, resource allocation, model quantization, batching, autoscaling, and multi-model serving.
• Experience with RAG systems, LLM applications, AI agents, or their supporting infrastructure.
• Strong software engineering fundamentals and a product-minded approach to platform design.
• Ability to make complex operational workflows reliable and approachable for users.

Nice to have

• Rust experience or interest in working with Rust-based backend systems.
• Experience running notebook environments such as JupyterHub, Kubeflow Notebooks, VS Code Server, or equivalent.
• Experience creating secure, scalable execution environments for user-defined functions or jobs.
• Experience deploying AI workloads on edge devices.
• Familiarity with model optimization techniques, including quantization, compilation, pruning, and hardware-aware serving.
• Experience with multi-tenant AI platforms, security controls, and model governance.
• Familiarity with observability and evaluation tooling for ML and LLM systems.

What Success Looks Like

You will help us evolve from manually operated AI infrastructure to a platform where users can train, evaluate, deploy, and operate models; work in managed notebooks; run functions; create agents; and configure RAG workflows - with the platform handling as much of the operational complexity as possible.

By submitting this application, I agree that my personal data will be collected, processed, and retained by the company solely for the purposes of managing and assessing my candidacy.