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.

Total Experience : 2-3 year

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).

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