MLOps Engineer
Fundamental3 months ago
Remote, IsraelSenior
Responsibilities
- Develop and manage scalable automated machine learning pipelines, CI/CD workflows, and orchestration frameworks.
- Design and implement scalable model-serving infrastructure and low-latency, high-throughput inference architectures.
- Implement A/B testing, canary releases, and rollback capabilities for model deployments.
- Build logging, alerting, and monitoring solutions for model development and reliability.
- Optimize GPU usage, autoscaling, and resource allocation.
- Design and maintain feature stores, data pipelines, and scalable storage solutions for training and inference data.
Requirements
- Bachelor’s or master’s degree in computer science, engineering, or a related field, or equivalent practical experience.
- 5+ years of experience as an MLOps engineer or in DevOps roles using MLOps platforms and frameworks.
- Experience building MLOps infrastructure from the ground up and managing data pipelines for model training and inference.
- Experience with model-serving frameworks for highly scalable, low-latency inference.
- Experience with Kubernetes on AWS, GCP, or Azure and infrastructure as code such as Terraform, Helm, or GitOps.
- Strong software engineering skills in Python, Bash, and Go.
- Experience with AI/ML systems security, compliance, model governance, and observability tools.
- Preferred experience with MLflow, Kubeflow, FastAPI, Databricks, Snowflake, Prometheus, Grafana, or Datadog, plus exposure to SRE practices or cloud security certifications.
Benefits
- Competitive compensation including salary and equity.
- Comprehensive health coverage for employees and dependents.
- Paid parental leave for all new parents, including adoptive and surrogate journeys.
- Relocation support for employees moving to one of the company’s office locations.
- Mission-driven, low-ego culture emphasizing diversity of thought, ownership, and bias toward action.
Tech Stack
AWSAzureBashDatabricksDatadogFastAPIGoGoogle Cloud PlatformGrafanaHelmKubernetesMLflowPrometheusPythonPyTorchSnowflakeTensorFlowTerraform
Categories
About Fundamental
Fundamental builds NEXUS, a large tabular AI model and platform for enterprise decision-making on structured data, delivered with deployment support. It sells to large companies, including Fortune 100 firms, through software licensing and forward-deployed data science services. Founded in 2024 by former DeepMind engineers and headquartered in San Francisco, the company is expanding industry use cases such as oil and gas, where it supports predictive analytics and operational planning.