Company: iAapTeck Software Labs
Role: AI ML Engineer
Project: BolBhidu Digital Solution
About Project:
This project involved designing and developing AI-driven backend systems and recommendation platforms focused on user behavior analytics, content personalization, and scalable ML deployment for BolBhidu Digital Solution. The architecture included building recommendation engines, Flask-based ML APIs, behavioral analytics solutions, and MLOps pipelines tailored for high-traffic digital engagement platforms utilizing Azure AI/ML services.
Key Technical Works:
- Developed recommendation systems utilizing Collaborative Filtering, K-Means, and DBSCAN algorithms for personalized content delivery.
- Built machine learning models using Python, Scikit-learn, and Pandas for user behavior analysis and engagement optimization.
- Developed scalable FastAPI (REST APIs) for real-time ML predictions with low-latency processing.
- Utilized Azure Machine Learning, Azure Kubernetes Service (AKS), Azure DevOps, and Azure Container Registry (ACR) for scalable ML deployments and MLOps workflows.
- Containerized ML applications using Docker and implemented CI/CD pipelines equipped with monitoring and model versioning.
- Designed A/B testing and analytics pipelines to continuously improve recommendation performance and customer retention.
Skill Sets:
Python, FastAPI, Scikit-learn, Pandas, NumPy, SQL, Collaborative Filtering, K-Means, DBSCAN, Azure Machine Learning, AKS, Azure DevOps, Azure Container Registry (ACR), Docker, CI/CD, Git/GitHub, REST APIs, MLOps, Behavioral Analytics, and A/B Testing.