ML Architect

Position : ML Architect Location : Chicago , IL Duration : 1 Year 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.