Team Lead - Python and AI

About the Role

Lead Python Developer

• Lead and mentor a team of Python developers, promoting best practices and fostering a collaborative work culture.
• Design, develop, and maintain efficient, reusable, and reliable Python code.
• Build scalable and high-performance data processing pipelines for machine learning and AI applications.
• Collaborate with cross-functional teams (design, product, QA, DevOps) to define, design, and deliver robust features.
• Perform code reviews, provide technical guidance, and ensure code quality through unit testing and best practices.
• Debug and resolve technical issues in production and development environments.
• Stay updated with emerging Python tools, trends, and technologies—especially in AI, data science, and cloud computing.
• Implement solutions integrating modern ML/AI capabilities including transformer-based models, vector databases, embeddings, and retrieval-augmented generation (RAGs).

Required Qualifications & Skills

• 5+ years of proven experience as a Python Developer in a professional setting.
• Bachelor’s degree in computer science, Software Engineering, or a related field.
• Strong command of Python programming and frameworks, with exposure to:
• Libraries such as NumPy, SciPy, Pandas, Dask, spaCy, NLTK, Scikit-learn, PyTorch
• Experience working with pre-trained LLMs, tokenization techniques, and transformer architectures
• Understanding of Vector DBs, embeddings, and RAG architectures
• Frontend development knowledge (HTML, CSS, JavaScript)
• Solid experience with SQL and NoSQL databases
• Excellent analytical thinking and problem-solving skills
• Strong communication skills and the ability to collaborate effectively in a team

Preferred Qualifications

• Experience with Python web frameworks such as Django, Flask, or Pyramid
• Familiarity with cloud platforms like AWS, Google Cloud Platform, or Microsoft Azure
• Hands-on experience with data science, machine learning workflows, and deployment pipelines
• Exposure to containerization tools (Docker, Kubernetes) and CI/CD practices