MLOps Engineer
Fundamental2 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
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