Senior MLOps Engineer: Build Production ML Platforms

BlackCube Labs; New York, Northern (NY, KY); Hybrid; USD 100,000 - 125,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability coverage

401(k) plan

Paid holidays

Parental leave

PTO

Job summary

Fractal invites a senior MLOps Engineer - Consultant to operationalize a portfolio of ML solutions. You will work with AI/ML teams to deliver inference services, data pipelines, and model lifecycle capabilities.

This hands-on role emphasizes production-grade systems, scalable architectures, and reusable ML platform components. You will design end-to-end ML platforms, work on batch and real-time serving, and ensure reliable deployment governance with MLflow and Databricks.

Qualifications

• Deep hands-on Python for data engineering and backend development.
• Production-grade backend services using FastAPI.
• Databricks stack including MLflow, Delta Lake, Databricks Workflows, and DABs.
• Experience with AWS services (SQS, EKS, Aurora PostgreSQL).
• Event-driven architectures using Kafka and/or SQS.
• Strong end-to-end ML lifecycle knowledge from training to inference.

Responsibilities

• Design and build FastAPI services exposing models to downstream applications.
• Implement queue-based asynchronous serving with SQS and Kafka.
• Containerize services with Docker and deploy on Kubernetes/EKS with observability.
• Develop end-to-end MLflow-based patterns for training, deployment, and inference.

Skills

Python

FastAPI

Databricks

MLflow

Delta Lake

Databricks Workflows

DABs

Spark

Docker

Kubernetes

AWS

Kafka

CI/CD

GitHub Copilot

Claude Code

Tools

GitHub Copilot

Claude Code

GitHub Actions

Jenkins

Docker

Kubernetes

Job description

Fractal invites a senior MLOps Engineer - Consultant to operationalize a portfolio of ML solutions. You will work with AI/ML teams to deliver inference services, data pipelines, and model lifecycle capabilities.

This hands-on role emphasizes production-grade systems, scalable architectures, and reusable ML platform components. You will design end-to-end ML platforms, work on batch and real-time serving, and ensure reliable deployment governance with MLflow and Databricks.