AI Software Development
Summary
Develop and deploy AI models (NLP, vision, recommendations) using frameworks like PyTorch and TensorFlow, integrate them into production systems, and maintain scalable data pipelines.
1. Core Responsibilities
a. AI Model Development
Design, train, test, and optimize machine learning (ML) or deep learning (DL) models (e.g., for NLP, computer vision, recommendation systems, etc.).
Select appropriate algorithms (e.g., CNNs, RNNs, Transformers, LLMs).
Fine-tune pre-trained models (e.g., OpenAI, Hugging Face, TensorFlow Hub models).
b. Data Preparation & Engineering
Collect, clean, and preprocess large datasets (structured or unstructured).
Build data pipelines for continuous training and model updates.
Use tools like Pandas, PySpark, or SQL for data manipulation.
c. System Integration
Integrate AI models into software systems, APIs, or web/mobile applications.
Collaborate with backend engineers to deploy models (e.g., using FastAPI, Flask, or REST endpoints).
Ensure scalable and efficient inference performance in production.
d. Research & Innovation
Stay current with new AI research papers and techniques.
Prototype new ideas (e.g., reinforcement learning, multimodal AI, generative AI).
Evaluate performance with metrics (accuracy, F1-score, BLEU, ROC-AUC, etc.).
🧰 2. Technical Skills Required
Category Key Tools / Frameworks
Programming Python, C++, Java, or R
ML Frameworks TensorFlow, PyTorch, Scikit-learn
Data Tools NumPy, Pandas, SQL, Apache Spark
Deployment Docker, Kubernetes, AWS/GCP/Azure, ONNX
MLOps MLflow, Airflow, Kubeflow, CI/CD
Version Control Git, GitHub/GitLab
Visualization Matplotlib, Seaborn, Tableau, Power BI