MLOps Engineer — Scalable AI Infra & Deployment, Equity

Fundamental; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Salary + equity

Health coverage for you and dependents

Parental leave for all

Relocation support

Mission-driven culture

Job summary

Fundamental, an AI company building the Nexus LTM, seeks an experienced MLOps engineer to own scalable ML pipelines, CI/CD, and robust model serving infrastructure. You will work across platforms to deploy low-latency, high-throughput inference and maintain reliable data pipelines for training and inference workflows.

You will design and implement monitoring, logging, and governance practices, while improving GPU utilization and autoscaling across AWS/GCP/Azure.

Qualifications

• 5+ years of experience as MLOps engineer or DevOps.
• Experience with MLOps platforms (MLflow, WandB) and frameworks (PyTorch, TensorFlow).
• Experience building MLOps infrastructure from the ground up.
• Experience with model serving frameworks (TorchServe, TF Serving, Triton, KServe).
• Experience in building and managing data pipelines for training and inference.
• Experience with Kubernetes on AWS/GCP/Azure and with IaC (Terraform, Helm, GitOps).
• Strong software engineering skills in Python, Bash, and Go.
• Experience in AI/ML systems security, compliance, and governance.
• Proficient with observability/monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry).

Responsibilities

• Develop and manage scalable ML pipelines, CI/CD workflows, and orchestration frameworks.
• Design and implement robust model serving infrastructure using TorchServe, TensorFlow, Triton.
• Develop scalable inference architectures with ultra-low latency and high throughput.
• Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback.
• Develop logging, alerting, and monitoring solutions to track model development and reliability.
• Improve GPU usage, enable autoscaling, and streamline resource allocation.
• Design, implement, and maintain feature stores, data pipelines, and scalable storage for large data volumes.

Skills

MLOps engineering

Python

Go

Bash

Kubernetes

Terraform

GitOps

TorchServe

TensorFlow Serving

Triton

MLflow

WandB

PyTorch

TensorFlow

Data pipelines

Observability

Prometheus

Grafana

Datadog

OpenTelemetry

Cloud platforms (AWS/GCP/Azure)

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Tools

Terraform

Helm

GitOps

Job description

Fundamental, an AI company building the Nexus LTM, seeks an experienced MLOps engineer to own scalable ML pipelines, CI/CD, and robust model serving infrastructure. You will work across platforms to deploy low-latency, high-throughput inference and maintain reliable data pipelines for training and inference workflows.

You will design and implement monitoring, logging, and governance practices, while improving GPU utilization and autoscaling across AWS/GCP/Azure.