Back End Developer

Instructions to apply is at the end of this job description!

About EasyInterviews.ai

EasyInterviews is revolutionizing the hiring landscape by replacing polished resumes with real-world skill simulations. We are pivoting our core focus from individual coaching to a comprehensive, AI-assisted recruiter platform that enables multi-round interviews, automated job-fit scoring, and legal compliance-conscious candidate evaluations.

We are looking for a Senior Backend Developer & Core Team Lead to serve as a foundational member of our technical leadership. You will take full ownership of our Python-Django stack, manage high-stakes AI integrations, and lead a growing team to deliver simultaneous projects across our ecosystem.

Position Overview

• Role: Senior Back-End Developer
• Location: Remote
• Type: Full-time
• Experience: 5+ years

Core Responsibilities

• Project Ownership & Leadership: Direct the backend development of the EasyInterviews and other internal platforms.
• Architecture & Development: Design, build, and maintain robust, scalable backend services and microservices using Python and Django.
• Cloud Infrastructure: Manage, optimize, and scale secure cloud infrastructure on AWS (including EC2, RDS, Lambda, ECS, S3, and CloudFront).
• API Engineering: Design and optimize secure, high-performance RESTful APIs and GraphQL endpoints for web and mobile interfaces.
• Asynchronous Processing: Manage distributed task queues and asynchronous workflows using Celery, RabbitMQ, or AWS SQS.
• Database & Performance Optimization: Handle schema design, write complex SQL queries, and implement caching layers (PostgreSQL, Redis) to ensure low-latency performance at scale.
• Code Quality & Leadership: Lead code reviews, enforce engineering best practices, write comprehensive unit/integration tests, and mentor junior to mid-level engineers.

Required Qualifications

• Backend Expertise: Minimum of 5 years of professional backend development experience, with a proven track record in Python and the Django ecosystem.
• AI/LLM Integration: Demonstrable experience building AI pipelines, integrating Large Language Models, and working with vector databases.
• AWS Proficiency: Deep hands-on experience with core AWS services, DevOps principles, and setting up CI/CD pipelines.
• Data Management: Strong proficiency in relational databases (specifically PostgreSQL), including indexing, query optimization, and data modeling.
• System Design: Demonstrable experience designing and maintaining distributed, high-traffic systems.
• Communication: Exceptional English communication skills, with the ability to collaborate effectively in an asynchronous, fully remote environment.

Preferred Qualifications

• Familiarity with containerization tools such as Docker and Kubernetes.
• Prior experience in early-stage SaaS, EdTech, or HRTech startups.

Benefits and Compensation

• Fully remote work environment with flexible scheduling.
• Competitive salary based on experience and market standards.
• Direct influence over product roadmap, system architecture, and technical stack decisions.
• A collaborative, high-autonomy culture focused on engineering excellence.

To Apply & Video Submission

To apply, please submit the following to [email protected]

1. Your resume.
2. A link to your GitHub profile or portfolio.
3. A brief summary of your experience managing Django/AWS applications at scale.
4. A 5-7 minute Loom (or similar recorded video) link walking us through one of your strongest AI/LLM projects.

In your recorded video, please screen-share and cover:

• System Architecture: The core problem you solved and the overall system design.
• The AI Pipeline: How you implemented embeddings, vector search/retrieval, RAG, and response generation based on user intent.
• Reliability & Monitoring: Your approach to guardrails, evaluation, context token management, and prompt construction/chaining.
• Code Walkthrough: A quick look at key implementation steps, explaining your design decisions, the trade-offs you considered, and why you made them.

(Note: The goal isn't to see every single detail, but to understand your technical approach and problem-solving process before we jump into a live interview.)