ML Architect - R01544683

About Brillio:

Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction.

Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.

Senior Data Science Lead

• Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

• Data Science Advanced: Data Science Lead

• Title: ML Architect
• Location: Chicago, IL
• Onsite model

• 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.

• 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.

• 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:
• Ability to collaborate with data scientists, engineers, and DevOps teams.
• Strong documentation skills for model architecture and deployment processes.

Know what it’s like to work and grow at Brillio: Click here