Remote ML Ops Engineer - Project-Based

Slalom; Dallas (TX); Hybrid; USD 166,400 - 208,000; Full time

Benefits offered by this job

Health coverage

401(k) match

Dental coverage

Vision coverage

Adoption assistance

Fertility assistance

Long‑term disability

Well‑being reimbursement

Job summary

Slalom seeks an AI/ML Engineer to design, deploy, and operate production‑grade ML and Generative AI solutions in a regulated enterprise environment. You will own end‑to‑end lifecycle, pipelines, and governance using Azure Databricks, MLflow, and CI/CD in a hybrid client‑facing setting.

You will collaborate with data scientists, data engineers, and business stakeholders to deliver scalable AI solutions such as forecasting, optimization, decision support, and advanced analytics at enterprise scale.

Qualifications

• 5+ years of hands‑on experience in Machine Learning Engineering or related roles.
• Experience deploying and operating ML solutions in enterprise environments.
• Strong Python development skills and ML frameworks.

Responsibilities

• Design, build, and deploy production‑grade ML and Generative AI solutions.
• Own end‑to‑end ML production lifecycle including data ingestion, feature engineering, deployment, monitoring.
• Develop and maintain MLOps pipelines using Azure Databricks, MLflow, Unity Catalog, and CI/CD processes.
• Implement scalable model serving architectures and APIs.
• Collaborate with data engineering to ensure robust data pipelines and feature stores.
• Establish model versioning, drift detection, retraining, and monitoring practices.

Skills

Python development

Machine Learning Engineering

MLOps

Azure Databricks

CI/CD pipelines

APIs & microservices

Docker

Git workflows

Communication

Education

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

Tools

Scikit-learn

PyTorch

TensorFlow

FastAPI/Flask

MLflow

Unity Catalog

Azure DevOps

Job description

Slalom seeks an AI/ML Engineer to design, deploy, and operate production‑grade ML and Generative AI solutions in a regulated enterprise environment. You will own end‑to‑end lifecycle, pipelines, and governance using Azure Databricks, MLflow, and CI/CD in a hybrid client‑facing setting.

You will collaborate with data scientists, data engineers, and business stakeholders to deliver scalable AI solutions such as forecasting, optimization, decision support, and advanced analytics at enterprise scale.