Job Role : ML Architect
Location : Chicago , IL(Onsite)(
Duration : 12 months (Possible Extension)
Linkedin & Passport Number is Mandatory for the submission
Job Description : -
The ML Architect designs and deploys scalable machine learning systems, ensuring models are production-ready, secure, and efficient. This role focuses on building ML pipelines, deploying models, and maintaining best practices for MLOps.
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.
