MLOps Engineer

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🔥 12 hours ago

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Fundamental

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2024

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Fundamental is an enterprise AI company that builds large tabular models (LTMs) such as NEXUS, pre-trained on billions of tables to detect patterns and predict outcomes from structured data. The company offers an enterprise-grade predictive analytics platform that can be deployed with minimal code or integrated deeply with cloud partners like AWS, emphasizing privacy, security, and scalability. Born from academic research and backed by major investors, Fundamental targets large organizations seeking to extract foresight from their databases and deploy predictive models at cloud scale.

📋 Description

• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc. • Develop scalable inference architectures optimized, with ultra-low latency and high throughput • Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities • Develop logging, alerting, and monitoring solutions to track model development, and reliability • Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency • Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of 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 MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..) • Experience building and designing MLOps infrastructure from the ground up • Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference • Experience in building and managing data pipelines to support both model training and inference • Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps) • Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code • Experience in AI/ML systems security, compliance, and model governance • Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry

🏖️ Benefits

• Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys • Relocation support for employees moving to join the team in one of our office locations • A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action