Engineering Lead
5 days ago
What will you be doing :
We are seeking a Lead Engineer (Engineering Lead) to lead our backend, data engineering, and infrastructure efforts. You'll 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 :
- Lead backend, data engineering, and infrastructure efforts, ensuring scalability and reliability.
- Drive microservices architecture and serverless implementations on AWS.
- Collaborate with AI engineers to integrate LLM-powered features efficiently.
- 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.
- 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.
- Lead and mentor backend and DevOps engineers.
- Foster a high-performance engineering culture with best coding practices, documentation, and DevOps workflows.
- Tech Stack : Python - Django, Node.js - Nest.js, RDS, DynamoDB, MongoDB, AWS -Lambda, API Gateway, Amplify, CI/CD, Microservices, DevOps, Vector Databases, LLM
- 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.
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