Full Stack Engineer

Key Responsibilities

• Design, develop, and deploy high-quality AI-driven web applications, ensuring performance, scalability, and maintainability across both frontend and backend.
• Build robust server-side applications using Python (FastAPI, Flask, or Django), integrating with databases, APIs, and AI/ML models.
• Create engaging and responsive user interfaces with TypeScript (React.js, Next.js, or Angular), ensuring an exceptional user experience.
• Collaborate with data scientists to integrate machine learning models into production systems, ensuring seamless data flow and model performance monitoring.
• Design, implement, and optimize ETL processes to efficiently collect, transform, and load large-scale datasets from multiple sources.
• Develop and consume RESTful and GraphQL APIs for communication between services and external systems.
• Work with AWS, Azure, or GCP to deploy, scale, and monitor applications and ETL pipelines in production environments.
• Implement automated testing strategies (unit, integration, and end-to-end) to maintain software quality and reliability.
• Work closely with cross-functional teams including data engineering, AI research, product management, and UX design to deliver impactful features.
• Stay current with emerging technologies, frameworks, and AI trends to propose innovative solutions and improve development practices.

Required Skills & Qualifications

• Experience: 6-10 years in full stack development, with at least 2-3 years in AI-driven application projects.
• Programming Expertise: Strong proficiency in Python for backend/API development and TypeScript for frontend applications.
• Frameworks: Hands-on experience with Python frameworks (FastAPI, Flask, Django) and TypeScript-based frontend frameworks (React, Angular, or Next.js).
• ETL & Data Engineering: Proven track record in designing and maintaining ETL pipelines and working with data processing tools (Airflow, dbt, or similar).
• Databases: Proficiency in relational and NoSQL databases (PostgreSQL, MySQL, MongoDB, or similar).
• AI/ML Integration: Experience integrating AI/ML models into production workflows, preferably with TensorFlow, PyTorch, or scikit-learn.
• Cloud & DevOps: Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines, along with cloud services (AWS Lambda, S3, GCP BigQuery, Azure Data Factory).
• Problem Solving: Strong analytical and troubleshooting skills, with the ability to address technical challenges in complex systems.
• Collaboration: Excellent communication skills and a track record of working in agile, cross-functional teams.