Overview
Hybrid
Depends on Experience
Contract - W2
Contract - Independent
Skills
Conflict Resolution
Data Science
Docker
Continuous Integration
Machine Learning Operations (ML Ops)
Electronic Engineering
Machine Learning (ML)
Continuous Delivery
Generative Artificial Intelligence (AI)
Microsoft Azure
Prototyping
Computer Science
Collaboration
Cloud Computing
Kubernetes
Artificial Intelligence
Problem Solving
Training
Management
Python
SQL
Linux
Job Details
AI/ML Engineer
Location Mundelein IL
Duration 3 months
(Only Independent contractors)
This is a Hybrid role (3 days onsite at Mundelein IL)
As an AI Engineer on the Data Science team, you will play a key role in productionizing machine learning models, building robust pipelines, and enhancing the overall AI platform. This role requires hands-on experience with Azure, Docker,and Azure Kubernetes Service (AKS), as well as strong knowledge of cloud-native MLOps best practices.
Responsibilities
- Design and implement scalable, cloud-native ML pipelines for production AI solutions.
- Collaborate with data scientists to operationalize ML models from prototypes to production.
- Manage deployment of ML models using Azure Machine Learning and AKS.
- Develop, containerize, and orchestrate services using Docker and Kubernetes.
- Optimize cloud data and compute architectures to ensure cost-effective and reliable deployments.
- Implement robust monitoring, logging, and CI/CD practices to support AI operations (MLOps).
- Work closely with enterprise cloud architects to align AI solutions with client s infrastructure standards.
- Contribute to the evolution of the best practices around AI/ML systems in production environments.
Qualifications
- Minimum 8 years of experience as a Data Scientist, with at least 3 years focused on machine learning engineering in cloud environments.
- Proven experience deploying ML models in Azure, preferably with Azure Machine Learning, Docker, and AKS.
- Hands-on experience building cloud-native pipelines for model training, scoring, and monitoring.
- Familiarity with GenAI concepts and tools (experience operationalizing GenAI is a plus).
- Proficiency in Python, SQL, and Linux-based development environments.
- Strong understanding of MLOps principles, CI/CD pipelines, and production-grade APIs.
- Effective communicator with strong problem-solving skills and ability to work across teams.
Education
- Bachelor s degree in Computer Science, Electronic Engineering, Data Science, or a related field.
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