Remote ML Ops Engineer - Project-Based AI Deployment

Slalom; Chicago (IL); Hybride; USD 110 000 - 138 000; Plein temps

Avantages offerts par ce poste

401(k) match

Health insurance

Dental and vision coverage

Adoption and fertility assistance

Well-being reimbursement

Résumé du poste

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a highly regulated enterprise environment. This role focuses on scalable, governed production systems and end-to-end ML lifecycle.

You will collaborate with data scientists, data engineers, and business stakeholders within Agile teams, leveraging Azure Databricks, MLflow, and CI/CD to deliver reliable AI solutions while ensuring governance, security, and responsible

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or related field.
• 5+ years of hands-on experience in Machine Learning Engineering, MLOps, or related software engineering roles for production AI systems.
• Experience deploying and operating ML solutions in enterprise environments.
• Strong Python development skills and proficiency with Scikit-learn, PyTorch, and TensorFlow.
• Experience developing and deploying APIs and microservices with FastAPI, Flask, MLflow Model Serving.
• Experience deploying front-end and back-end apps in Azure environments.
• Deep expertise with Azure Databricks (Spark, Delta Lake, Unity Catalog, Feature Store).
• Hands-on MLflow for experiment tracking, model registry, packaging, and automated deployment.
• CI/CD pipelines using Azure DevOps or GitHub Actions.
• Git-based workflows, branching, and PR processes.
• Docker for containerized apps; Azure services like ADLS, Key Vault, Monitor.
• Excellent communication and problem-solving; able to lead in Agile teams.

Responsabilités

• Design, build, and deploy production-grade ML and Generative AI solutions.
• Own end-to-end ML production lifecycle: data ingestion, feature engineering, deployment, monitoring.
• Develop MLOps pipelines using Azure Databricks, MLflow, Unity Catalog, and CI/CD.
• Implement scalable model-serving architectures (real-time APIs, batch inference, feature stores).
• Convert data science prototypes into production-ready software.
• Collaborate with data engineering to meet performance and quality requirements.
• Establish model versioning, reproducibility, deployment automation, monitoring, and retraining.
• Build online and batch endpoints, compute infrastructure, monitoring, and dashboards.
• Ensure compliance with data governance, privacy, security, and responsible AI standards.
• Communicate technical decisions to both technical and non-technical stakeholders.

Connaissances

ML Engineering

Enterprise ML deployment

Python

Scikit-learn

PyTorch

TensorFlow

FastAPI

Flask

MLflow

Azure

Azure Databricks

Apache Spark

Delta Lake

Unity Catalog

Feature Store

CI/CD

Git workflows

Docker

ADLS

Key Vault

Monitor

Communication

Leadership

Formation

Bachelor’s or Master’s degree in a relevant field

Outils

Python

Description du poste

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a highly regulated enterprise environment. This role focuses on scalable, governed production systems and end-to-end ML lifecycle.

You will collaborate with data scientists, data engineers, and business stakeholders within Agile teams, leveraging Azure Databricks, MLflow, and CI/CD to deliver reliable AI solutions while ensuring governance, security, and responsible