We are seeking a skilled Python Developer with expertise in Artificial Intelligence and Machine Learning to join our dynamic team. The ideal candidate will design, develop, and deploy AI-powered applications, pipelines, and solutions, ensuring performance, scalability, and maintainability in production environments.Key Responsibilities:
- Design, develop, test, and deploy scalable Python applications.
- Write clean, efficient, and modular code integrating AI/ML models into production systems.
- Develop REST APIs for AI services and manage data pipelines for ML workflows.
- Build, train, evaluate, and optimize machine learning models using libraries such as TensorFlow, PyTorch, or scikit-learn.
- Deploy models using ML frameworks or cloud-based AI services (AWS SageMaker, Azure ML, or GCP AI Platform).
- Conduct feature engineering, model selection, hyperparameter tuning, and performance evaluation.
- Work with structured and unstructured data (CSV, JSON, images, text, etc.).
- Develop data preprocessing pipelines ensuring data quality and readiness for modelling.
- Participate in code reviews, design discussions, and agile ceremonies (stand-ups, sprint planning).
- Stay updated with the latest AI/ML trends, architectures, and algorithms.
- Containerize applications using Docker.
- Deploy AI models in production using CI/CD pipelines.
Required Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related fields.
- 2-3 years of hands-on experience in Python development.
- Strong understanding of AI/ML concepts, algorithms, and workflows.
- Experience with machine learning libraries (TensorFlow, PyTorch, Keras, scikit-learn).
- Proficiency in data manipulation libraries (Pandas, NumPy) and visualization tools (Matplotlib, Seaborn).
- Experience deploying AI models into production systems.
- Familiarity with API development using frameworks like Flask or FastAPI.
- Understanding of SQL and NoSQL databases.
- Experience in Natural Language Processing (NLP) or Computer Vision (CV) projects.
- Knowledge of MLOps practices, ML model monitoring, and continuous training.
- Familiarity with big data tools (Spark, Hadoop) is a plus.
- Working experience with cloud platforms (AWS, Azure, GCP) for AI services.
- Exposure to Deep Learning architectures (CNNs, RNNs, Transformers).
