Senior MLOps Engineer — Platform & Deployment Expert

Fractal; New York (NY); Presencial; USD 100.000 - 125.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability plans

401(k) after 30 days

11 paid holidays

12 weeks of Parental Leave

Free time PTO

Descripción de la vacante

Fractal seeks a senior MLOps Engineer on a consulting basis to operationalize a portfolio of ML solutions in purchase and underwriting. You will collaborate with our AI/MLOps team and partner with Data Science and Data Engineering to deliver inference services, data pipelines, feature frameworks, and model lifecycle management.

Focus areas include building production-grade backend services with FastAPI, Databricks platforms (MLflow, Delta Lake, Workflows, DABs), and containerized deployments on

Formación

• Deep hands-on Python experience for data engineering and backend development.
• Strong background building production-grade backend services with FastAPI.
• Hands-on expertise with Databricks: MLflow, Delta Lake, Workflows, DABs.
• Spark-based distributed processing experience.
• Proven end-to-end MLflow training and inference architectures across environments.
• Experience with AWS services including SQS, EKS, and Aurora PostgreSQL.
• Event-driven and asynchronous architectures using Kafka and/or SQS.
• Solid understanding of the full ML lifecycle: feature engineering, training, deployment, monitoring, retraining.
• Ability to craft architecture diagrams, design docs, runbooks, and deployment plans.
• Strong Docker and Kubernetes fundamentals; familiarity with CI/CD (GitHub Actions, Jenkins).
• Hands-on use of GitHub Copilot and Claude Code in daily workflows.
• Excellent written and verbal communication for collaboration with stakeholders.

Responsabilidades

• Design and build FastAPI services exposing models with contracts, auth, input validation, and observability.
• Implement asynchronous serving patterns with SQS/Kafka for higher throughput.
• Containerize services with Docker and deploy to Kubernetes/EKS with production-grade observability.
• Define model inference patterns for batch and real-time use cases.
• Create end-to-end MLflow-based training, tracking, registry, deployment, and inference workflows.
• Manage reproducible ML workflows and production-ready data pipelines on Databricks.

Conocimientos

Python

FastAPI

Docker & Kubernetes

AWS

Kafka

MLflow

Databricks

Spark

CI/CD

GitHub Copilot

Claude Code

Herramientas

Databricks

MLflow

Delta Lake

Databricks Workflows

Databricks Asset Bundles (DABs)

Spark

Kubernetes

Docker

SQS

Kafka

Jenkins

GitHub Actions

Descripción del empleo

Fractal seeks a senior MLOps Engineer on a consulting basis to operationalize a portfolio of ML solutions in purchase and underwriting. You will collaborate with our AI/MLOps team and partner with Data Science and Data Engineering to deliver inference services, data pipelines, feature frameworks, and model lifecycle management.

Focus areas include building production-grade backend services with FastAPI, Databricks platforms (MLflow, Delta Lake, Workflows, DABs), and containerized deployments on