Engineering Lead

3 days ago

Bengaluru, India PinnacleU HR Full time

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 :- 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.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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