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.