Engineering Lead - Backend Development

We are looking for a Lead Engineer (Engineering Lead) to lead our backend, data engineering, and infrastructure efforts. Youll be responsible for building scalable data pipelines, backend services, and cloud infrastructure to power our AI-driven platform. Leading the entire engineering team. Help founders foster a high-performance engineering culture with best coding practices, documentation, and DevOps workflows.

Key Responsibilities :

Tech Leadership & Strategy :

- Drive microservices architecture and serverless implementations on AWS.

- Collaborate with AI engineers to integrate LLM-powered features efficiently.

Backend & Data Engineering :

- Architect and develop high-performance APIs using Django & NodeJS.

- Build scalable ETL pipelines to collect, clean, and update university and course data.

- Work with RDS, DynamoDB, and MongoDB for structured and unstructured data management.

Cloud & DevOps :

- Implement CI/CD pipelines, containerization (Docker, Kubernetes), and AWS infrastructure (Lambda, API

Gateway, ALB, Amplify etc.).

- Ensure scalability, security, and cost efficiency of cloud infrastructure.

- Automate deployments and optimize system performance.

Team Leadership & Growth :

- Lead and mentor backend and DevOps engineers.

- Foster a high-performance engineering culture with best coding practices, documentation, and DevOps workflows.

What makes you a great fit?

- 5- 8 years of experience in backend development, data engineering, and cloud infrastructures.

- Ability to work in a fast-paced startup environment and make key architectural decisions.

- Expertise in Python (Django) and Node.js (Nest.js) for backend development.

- Strong experience with AWS (Lambda, API Gateway, RDS, DynamoDB, CI/CD, Microservices, DevOps).

- Experience building ETL pipelines and managing large datasets for AI-powered applications.

- Hands-on knowledge of infrastructure as code (Terraform, AWS CDK) and containerization (Docker/Kubernetes) is preferred.

- Experience working with AI/ML teams, particularly in RAG and LLM fine-tuning workflows, is a plus.