Technical Background: Strong experience in Python Backend Development (FastAPI/Django/Flask) and a solid grasp of ML libraries (Scikit-Learn, NumPy, Pandas).

Teaching Philosophy: A "project-first" approach moving students from simple scripts to containerized, secure APIs

Soft Skills: Ability to simplify complex architectural concepts like Dependency Injection or Dimensionality Reduction for undergraduates.

Job Profile :We are looking for a Python & Backend Developer Trainer to lead the core technical track of our AI-Augmented program. This role is designed for a practitioner who can teach Python not just as a syntax, but as the backbone for scalable APIs, robust backend systems, and Machine Learning workflows.

Key Responsibilities

- Full-Stack Mentorship: Teach students to move from basic Python scripts to building production-ready, layered API architectures.
- Backend Architecture: Instruction on modern backend patterns using FastAPI, SQLAlchemy, and Pydantic.
- ML Integration: Bridge the gap between data science and engineering by teaching data preprocessing and model implementation.

Technical Competencies Required 1. Core Python & Mathematical Computing

- Proficiency in Python fundamentals (Data structures, OOP, Exception handling).




- Experience with NumPy for vector/matrix operations and solving linear equations.
- Ability to teach problem-solving through flowcharts and algorithmic logic.

2. Modern Backend Development (FastAPI/SQLAlchemy)

- Layered Architecture: Expertise in routers, services, schemas, and dependency injection.
- Database Management: Strong SQL fundamentals and ORM concepts (SQLAlchemy sessions, migrations, N+1 query optimization).
- API Standards: CRUD patterns, Pydantic modeling, middleware, and OpenAPI documentation.

3. Security & DevOps Foundations

- Identity Management: Deep understanding of JWT, OAuth2 flows, and Role-Based Access Control (RBAC).
- Hardening: Implementation of CORS, rate limiting, and input sanitization.
- Deployment: Knowledge of ASGI (Uvicorn/Gunicorn), Docker basics, and workplace configuration (.env).

4. Applied Machine Learning

- Ability to guide students through the ML Workflow: from data cleaning and feature engineering to train-test splits.
- Hands-on experience with Supervised & Unsupervised algorithms (Regression, k-NN, Decision Trees, k-Means).
- Intuition for model evaluation, including the Bias-Variance tradeoff and PCA.

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📌 Python senior trainer (Teaching Role) (Mangalore)
🏢 Talent Corner HR Services
📍 Mangalore