AI/ML Architect

Overview

On Site
Depends on Experience
Contract - W2
Contract - 6 Month(s)

Skills

Artificial Intelligence
Cloud Computing
Continuous Integration
DevOps
Collaboration
Computer Science
Conflict Resolution
Continuous Delivery
Data Science
Docker
Electronic Engineering
Generative Artificial Intelligence (AI)
Kubernetes
Linux
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Management
Microsoft Azure
Problem Solving
Python
Training
SQL
Prototyping

Job Details

AI/ ML Architect

Location :Northbrook, IL ( 100% onsite. No exceptions. Exceptional candidates can work 2 days a week from home)

Duration 6 Months

Interviews Immediate.

Must-Have Skills:

Microsoft Azure ML Architecting Experience 40% weightage

Microsoft Azure AI Architecting Experience 40% weightage

Microsoft Azure DevOps Architecting Experience 20% weightage

This is a Hybrid role (3 days onsite) Northbrook, IL

Must-Have Skills:

  • Microsoft Azure ML Architecting Experience 40% weightage
  • Microsoft Azure AI Architecting Experience 40% weightage
  • Microsoft Azure DevOps Architecting Experience 20% weightage


As an AI Architect 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 12 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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