

Artificial Intelligence Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior AI/ML Engineer for 4+ months in Mundelein, IL (Hybrid). Requires 8+ years in Data Science, 3+ years in cloud ML engineering, expertise in Azure, Docker, AKS, and MLOps principles. Bachelor's in a related field is essential.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
July 30, 2025
π - Project duration
More than 6 months
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ποΈ - Location type
Hybrid
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π - Contract type
Unknown
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π - Security clearance
Unknown
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π - Location detailed
Mundelein, IL
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π§ - Skills detailed
#Azure Machine Learning #Python #SQL (Structured Query Language) #Deployment #Docker #ML (Machine Learning) #Linux #Data Science #Cloud #Monitoring #Computer Science #Azure #Kubernetes #Logging #Scala #AI (Artificial Intelligence)
Role description
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We are actively looking for a Senior AI/ML Engineer. If you are actively looking for a new job please share your profile.
Role: Senior AI/ML Engineer
Duration: 4+ Months
Location: Mundelein, IL
This is a Hybrid role (3 days onsite)
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
β’ Design and implement scalable, cloud-native ML pipelines for production AI solutions.
β’ Collaborate with data scientists to operationalize ML models from prototypes to production.
β’ Manage deployment of ML models using Azure Machine Learning and AKS.
β’ Develop, containerize, and orchestrate services using Docker and Kubernetes.
β’ Optimize cloud data and compute architectures to ensure cost-effective and reliable deployments.
β’ Implement robust monitoring, logging, and CI/CD practices to support AI operations (MLOps).
β’ Work closely with enterprise cloud architects to align AI solutions with clientβs infrastructure standards.
β’ Contribute to the evolution of the best practices around AI/ML systems in production environments.
Qualifications
β’ Minimum 8 years of experience as a Data Scientist, with at least 3 years focused on machine learning engineering in cloud environments.
β’ Proven experience deploying ML models in Azure, preferably with Azure Machine Learning, Docker, and AKS.
β’ Hands-on experience building cloud-native pipelines for model training, scoring, and monitoring.
β’ Familiarity with GenAI concepts and tools (experience operationalizing GenAI is a plus).
β’ Proficiency in Python, SQL, and Linux-based development environments.
β’ Strong understanding of MLOps principles, CI/CD pipelines, and production-grade APIs.
β’ Effective communicator with strong problem-solving skills and ability to work across teams.
Education
Bachelorβs degree in Computer Science, Electronic Engineering, Data Science, or a related field.
Thank You
Satti Reddy