What are the responsibilities and job description for the ML Architect position at Codebase Inc?
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.
