AI Full-Stack Engineering

Job Summary
AI Full-Stack Engineer will bridge the gap between data science and production-grade AI systems. You will design, develop, and deploy end-to-end machine learning solutions that directly impact our 1,000+ retail stores and omni-channel platforms. From exploratory data analysis to optimizing real-time APIs, you'll own the entire AI lifecycle while collaborating with cross-functional teams to drive business outcomes.
Key Responsibilities
1. End-to-End AI Development
Data Analysis & Modeling:
- Perform exploratory data analysis (EDA) on retail datasets (sales, inventory, customer behavior).
- Build and validate ML models (recommendation systems, demand forecasting, customer churn prediction) using frameworks like TensorFlow, PyTorch, or Scikit-learn.
Production Deployment:
- Containerize models using Docker and deploy scalable APIs (FastAPI/Flask) on cloud platforms
- Optimize models for low-latency inference (e.g., quantization, ONNX runtime).
MLOps & Infrastructure:
- Design CI/CD pipelines (GitHub Actions, Jenkins) for automated testing and deployment.
- Implement monitoring tools (Prometheus, Grafana) to track model performance, data drift, and system health.
2. Cross-functional Collaboration
- Partner with Data Engineers to improve data pipelines (Kafka, Spark) and feature stores.
- Work with Product Managers to translate business requirements into technical specifications (e.g., real-time personalized recommendations).
- Explain model outcomes to non-technical stakeholders via dashboards (Superset).
3. Optimization & Innovation
- Reduce cloud costs by optimizing resource allocation (GPU/CPU usage, spot instances).
- Stay updated with cutting-edge tools (e.g., LangChain for LLM integration) and propose innovative solutions.