Job Title: AI Engineer (Python & Machine Learning)

Location: Any UST
Experience Level: 5 - 7 Yrs
Department: AI & Data Science

Role Overview:

We are seeking two highly skilled AI Engineers with strong proficiency in Python and a solid understanding of AI/ML concepts. You will be responsible for developing, training, and optimizing machine learning models, collaborating with cross-functional teams, and contributing to scalable AI solutions in production. This is a hands-on role requiring both theoretical knowledge and practical experience in building AI systems.

Roles and Responsibilities:

Design, develop, and optimize machine learning and AI models using Python and modern ML frameworks.

Collaborate with data scientists, software engineers, and product managers to define AI solution requirements.

Perform data preprocessing, cleaning, and feature engineering to prepare datasets for modeling.

Conduct model training, validation, and performance evaluation using appropriate metrics.

Tune hyperparameters and experiment with model architectures to enhance accuracy and efficiency.

Document model workflows, methodologies, and code to ensure transparency and reproducibility.

Assist in deploying AI models to production, monitoring performance, and refining them over time.

Stay informed on the latest research papers, tools, and best practices in AI and machine learning.

Participate in code reviews and maintain high standards for clean and maintainable code.

Must-Have Skills:

Strong proficiency in Python, with a focus on AI/ML development.

Solid understanding of machine learning algorithms (e.g., regression, classification, clustering, deep learning).

Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.

Proficiency in data preprocessing and feature engineering techniques.

Familiarity with model evaluation metrics (accuracy, precision, recall, AUC, etc.).

Experience working with Jupyter Notebooks and collaborative development tools (Git, etc.).

Strong problem-solving skills and ability to work in a fast-paced environment.

Good-to-Have Skills:

Experience with model deployment tools and platforms (e.g., Docker, Flask, FastAPI, AWS SageMaker).

Knowledge of MLOps practices and model lifecycle management.

Familiarity with NLP, computer vision, or time series modeling.

Understanding of distributed computing frameworks (e.g., Spark, Dask).

Experience in using experiment tracking tools like MLflow or Weights & Biases.

Exposure to cloud services (AWS, Azure, or GCP) for AI model development and deployment.