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01/07/2025

ML Architect

Contract

Job Description

Position : ML Architect
Location : Chicago , IL
Duration : 1 Year
Experience: 7-9+ years

If you are Interested, Drop me your Resume to  thomas@innovitusa.com (or) you can Reach 
me on +1 408-755-2428.

 
Qualifications:
Bachelor's or Master’s Degree in Computer Science, Data Engineering, Machine Learning, or related field.
Preferred: Certification in cloud platforms (Azure, AWS, GCP) or MLOps.
Experience:
7-9+ years of experience in machine learning, software engineering, or data engineering.
3-4 years of experience deploying ML models in production environments.
Experience with cloud platforms, MLOps practices, and large-scale systems in the QSR or retail industry is highly beneficial.
Key Skills:
System Design & Architecture:
Experience designing and deploying machine learning systems that scale across thousands of locations.
Building real-time recommendation engines for digital ordering platforms.
Model Deployment & MLOps:
Proficiency in MLOps practices for continuous integration, delivery, and deployment (CI/CD).
Familiarity with cloud-based ML services (Azure ML, SageMaker, GCP Vertex AI).
Experience in containerization (Docker) and orchestration (Kubernetes).
Knowledge of serverless computing and cloud-native services.
Inventory & Supply Chain Optimization:
Building ML solutions for supply chain forecasting, inventory optimization, and waste reduction.
Fraud Detection & Risk Management:
Experience in implementing fraud detection systems for payment processing and loyalty programs.

Recommendation Systems:
Developing personalized upsell and cross-sell recommendations for digital ordering systems.
Performance Optimization:
Ability to optimize model performance and latency for real-time applications.
Experience with distributed computing frameworks (Spark, Dask).
Security & Compliance:
Ensuring deployed models comply with data privacy regulations (e.g., GDPR, CCPA) and security best practices.
Collaboration & Documentation:
Ability to collaborate with data scientists, engineers, and DevOps teams.
Strong documentation skills for model architecture and deployment processes.