Artificial Intelligence Developer

Roles and Responsibilities

Model Development & Deployment

• Design, develop, and optimize machine learning and deep learning models for real-world applications (classification, regression, NLP, vision, recommendation systems, etc.).
  
• Train models using large datasets and evaluate their performance using appropriate metrics.
  
• Deploy models to production using tools like Flask/FastAPI, Docker, and cloud services (AWS/GCP/Azure).
  

Data Collection & Preprocessing

• Collect, clean, transform, and manage structured and unstructured datasets (CSV, JSON, images, text, etc.).
  
• Perform data wrangling, feature engineering, and exploratory data analysis (EDA).
  

Algorithm Research & Implementation

• Research and implement AI/ML algorithms suited to business use cases.
  
• Leverage existing libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Hugging Face) and fine-tune pre-trained models when applicable.
  

NLP & Computer Vision (As per domain focus)

• For NLP: Build and integrate models for sentiment analysis, chatbots, text summarization, translation, and entity recognition.
  
• For Vision: Apply techniques for object detection, image classification, OCR, face recognition, etc.
  

API & Application Integration

• Create and manage APIs to expose AI model functionalities to frontend/backend systems.
  
• Integrate models into enterprise applications, dashboards, or web platforms.
  

Model Monitoring & Maintenance

• Monitor model performance over time, handle concept drift, and retrain models as needed.
  
• Maintain and document ML pipelines, model versions, and deployment logs.
  

Collaboration & Agile Participation

• Work in cross-functional teams with data engineers, frontend/backend developers, and product owners.
  
• Participate in Agile ceremonies like standups, sprint planning, and demos.