ML Architect

Role: ML Architect(Hybrid) Location: Chicago , IL(locally available or near by to Chicago , IL) 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: o Experience designing and deploying machine learning systems that scale across thousands of locations. o Building real-time recommendation engines for digital ordering platforms. Model Deployment & MLOps: o Proficiency in MLOps practices for continuous integration, delivery, and deployment (CI/CD). o Familiarity with cloud-based ML services (Azure ML, SageMaker, GCP Vertex AI). o Experience in containerization (Docker) and orchestration (Kubernetes). o Knowledge of serverless computing and cloud-native services. Inventory & Supply Chain Optimization: o Building ML solutions for supply chain forecasting, inventory optimization, and waste reduction. Fraud Detection & Risk Management: o Experience in implementing fraud detection systems for payment processing and loyalty programs. Recommendation Systems: o Developing personalized upsell and cross-sell recommendations for digital ordering systems. Performance Optimization: o Ability to optimize model performance and latency for real-time applications. o Experience with distributed computing frameworks (Spark, Dask). Security & Compliance: o Ensuring deployed models comply with data privacy regulations (e.g., GDPR, CCPA) and security best practices. · Collaboration & Documentation: o Ability to collaborate with data scientists, engineers, and DevOps teams. o Strong documentation skills for model architecture and deployment processes.