Remote ML Ops Engineer — Project-Based, Enterprise AI

Slalom; Miami (FL); Hybrid; USD 110,208 - 137,760; Full time

Benefits offered by this job

401(k) with match

Well-being reimbursement

subsidized health, dental, vision

Job summary

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a regulated enterprise. You will transform validated models into scalable, governed systems with end-to-end lifecycle ownership.

The role emphasizes hands-on work with MLOps, model deployment on Azure Databricks, and cloud-native platforms within agile teams to deliver reliable AI at scale.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field such as CS, Math, or Data Science.
• Hands-on experience deploying production ML/AI systems.
• Proficiency in Python and ML libraries (Scikit-learn, PyTorch, TensorFlow).
• Experience deploying APIs/microservices (FastAPI, Flask) and using MLflow Model Serving.

Responsibilities

• Design, build, and deploy production-grade ML/Generative AI solutions.
• Own end-to-end ML production lifecycle: ingestion, features, deployment, monitoring.
• Develop MLOps pipelines with Azure Databricks, MLflow, Unity Catalog.
• Implement scalable model-serving architectures and online/batch endpoints.
• Collaborate with data engineers to ensure data quality and performance.

Skills

Python

MLOps

Azure Databricks

MLflow

FastAPI/Flask

Docker

Git/GitHub Actions

CI/CD

Spark/Delta Lake

Education

Bachelor’s or Master’s in CS/SE/Math/Data Science

Tools

Azure Databricks

MLflow

Unity Catalog/Feature Store

Docker

Azure DevOps

GitHub Actions

Flask/FastAPI

Job description

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a regulated enterprise. You will transform validated models into scalable, governed systems with end-to-end lifecycle ownership.

The role emphasizes hands-on work with MLOps, model deployment on Azure Databricks, and cloud-native platforms within agile teams to deliver reliable AI at scale.