F

Fundamental

Predictive analytics platform for structured data

DevOps Team Lead

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • 7+ years of experience in cloud infrastructure and DevOps, including 5+ years in a technical leadership role.
  • A proven track record of building and leading high-performing infrastructure teams.
  • Strong experience with Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
  • Deep expertise in Kubernetes, including multi-cluster management, GPU workload optimization, resource scheduling and autoscaling, network policies, and security.
  • Extensive experience with cloud networking, including virtual private cloud design, load balancer configuration, network security and segmentation, and cross-cloud networking solutions.
  • Strong continuous integration and continuous delivery expertise, preferably with GitHub Actions.
  • Proficiency in Terraform.
  • Proficiency with GitOps tools, preferably ArgoCD.
  • At least 3 years of experience with Python.
  • Experience with monitoring and observability tools.
  • Experience with FinOps practices and cloud cost optimization.
  • Ability to translate technical concepts for diverse audiences.

Responsibilities

  • Lead and mentor a team of DevOps engineers, fostering technical growth and collaboration.
  • Define and drive the infrastructure roadmap aligned with company objectives.
  • Architect and oversee cloud infrastructure design and implementation.
  • Establish best practices, standards, and processes for infrastructure development and operations.
  • Partner with Engineering, Research, and FDE to align infrastructure capabilities with business needs.
  • Drive the evolution of Kubernetes clusters optimized for GPU workloads, production SaaS hosting, and varied enterprise deployment models.
  • Champion GitOps practices using ArgoCD for continuous deployment.
  • Establish infrastructure-as-code standards using Terraform.
  • Define monitoring and observability strategy for distributed systems.
  • Collaborate with machine learning engineers to optimize infrastructure for model training and serving.
  • Own infrastructure reliability, performance, and security posture.
  • Implement and maintain cost optimization strategies for cloud resources.

Desired Qualifications

  • Experience with machine learning workflow tooling such as MLflow, Kubeflow, or similar.
  • Experience with FastAPI and backend applications.
  • Familiarity with data platforms such as Databricks or Snowflake.
  • Experience with site reliability engineering practices or cloud security certifications.
  • Hands-on experience with Prometheus, Grafana, or Datadog.
  • Experience scaling infrastructure for artificial intelligence and machine learning startups.

About the Company

Fundamental

Predictive analytics platform for structured data

Fundamental offers an enterprise-grade predictive intelligence product called NEXUS, a Large Tabular Model that analyzes structured data from spreadsheets, databases, and CRM systems. It learns patterns directly from raw tabular data, eliminating the need for extensive feature engineering, and combines forecasting, classification, anomaly detection, and scenario simulation in one model. NEXUS is deployed through the AWS dashboard via a strategic partnership and was trained on Amazon SageMaker HyperPod to handle datasets with billions of rows, serving industries like financial services, healthcare, retail, and energy with seven-figure contracts for use cases like demand forecasting, price prediction, and churn analysis. The goal is to turn raw data into actionable insights that improve forecasting accuracy, reduce risk, and optimize business outcomes, setting Fundamental apart by focusing on structured data rather than unstructured text or images.

Company size
51-200
Company stage
N/A
Total funding
$255M
Headquarters
San Francisco, CA
Founded
2024

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