Python AI -Developer

Developer - Python with AI

Roles and Responsibilities:

• AI/ML Solution Development:

  Design, develop, and implement end-to-end AI/Machine Learning solutions using

  Python

  , focusing on areas such as predictive modeling, natural language processing (NLP), computer vision, or recommendation systems.

• Python Programming:

  Write clean, efficient, scalable, and well-documented Python code for AI model development, data preprocessing, feature engineering, and integration into larger applications.

• Data Handling:

  Work with various data sources, perform data extraction, transformation, and loading (ETL), and ensure data quality and integrity for AI model training and inference.

• Model Training & Evaluation:

  Develop, train, and evaluate machine learning models. Perform hyperparameter tuning and model selection to achieve optimal performance.

• Deployment & MLOps:

  Assist in deploying AI models into production environments. Contribute to MLOps practices, including model monitoring, versioning, and retraining strategies.

• API Development:

  Build and integrate RESTful APIs to expose AI functionalities for consumption by other applications.

• Analysis & Research:

  Conduct research into new AI/ML techniques and technologies. Analyze existing systems and business problems to identify opportunities for AI integration.

• Troubleshooting:

  Perform in-depth troubleshooting, debugging, and issue resolution for AI models, Python applications, and data pipelines.

• Collaboration:

  Work closely with data scientists, data engineers, software developers, and business stakeholders to translate AI concepts into deployable solutions.

Preferred Candidate Profile:

• Python Expertise:

  Strong proficiency in

  Python programming

  , including experience with relevant libraries for data manipulation (e.g., Pandas, NumPy) and scientific computing.

• AI/ML Fundamentals:

  Solid understanding of core

  Artificial Intelligence and Machine Learning

  concepts, algorithms (e.g., regression, classification, clustering), and model evaluation metrics.

• Machine Learning Frameworks:

  Hands-on experience with at least one major machine learning framework (e.g.,

  Scikit-learn, TensorFlow, Keras, PyTorch

  ).

• Data Science Libraries:

  Familiarity with data visualization libraries (e.g., Matplotlib, Seaborn) for exploratory data analysis.

• API & Integration:

  Experience with developing and consuming RESTful APIs for integrating AI models into applications.

• Problem-Solving:

  Excellent analytical and problem-solving skills with a creative approach to complex AI challenges.

• Communication:

  Strong verbal and written communication skills to articulate technical concepts and AI solutions clearly to both technical and non-technical audiences.

• Education:

  Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.