MI Learning

Introduction

A Machine Learning Engineer designs, builds, and deploys scalable AI systems by implementing algorithms, processing large datasets, and creating automated learning models. This role bridges data science and software engineering to turn prototypes into production-ready applications that drive business value, often involving data pipelines, model training, and performance monitoring.

Responsibilities

• Develop & Implement: Create and implement ML algorithms, models (like deep learning), and applications.
• Data Management: Acquire, clean, preprocess, and analyze large datasets for training.
• Model Building: Design model architectures, perform training/retraining, and conduct experiments.
• Deployment (MLOps): Build robust data pipelines and deploy models into production environments (APIs, web apps).
• Testing & Evaluation: Run tests, evaluate model performance (accuracy, loss), and document findings.
• Collaboration: Work with data scientists, engineers, and product teams to align ML with business goals.
• Maintenance & Optimization: Monitor deployed models, identify bugs, and continuously improve performance and efficiency.
• Research: Stay updated on new algorithms, frameworks (TensorFlow, PyTorch, Scikit-learn), and advancements.

Core Skills

• Strong programming (Python).
• Deep understanding of ML algorithms, statistics, and data science.
• Expertise in ML frameworks (TensorFlow, PyTorch).
• Data preprocessing & feature engineering.
• Problem-solving and analytical skills.

Role Context

• Acts as a bridge between data scientists (model creation) and software/data engineers (system building).
• Focuses on making models work in real-world, scalable applications.