Engineering Lead

5 days ago


Bengaluru, Karnataka, India Awign Full time

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.
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.
Required Skills :
  • Tech Stack : Python - Django, Node.js - Nest.js, RDS, DynamoDB, MongoDB, AWS -Lambda, API Gateway, Amplify, CI/CD, Microservices, DevOps, Vector Databases, LLM
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.
(ref:hirist.tech)
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