Lead Backend Engineer

About the Role

Were hiring a Lead Backend Engineer to serve as the senior technical leader for backend architecture, owning the design, performance, and scalability of the core systems that power our AI-driven platform. This is a backend-first role focused on building high-throughput services, scalable APIs, distributed processing layers, and robust data flows that support video, audio, and text-based interactions at scale for hundreds of thousands of users.

You will work closely with our ML and DevOps teams to integrate backend services with multimodal LLM pipelines and cloud infrastructure, but you will not be responsible for model development or infrastructure ownership. Your role is to ensure clean interfaces, reliable dataflow, and sound architectural choices that enable these systems to operate cohesively in production at scale.

This is a high-impact technical leadership position where you will shape backend architecture, set engineering standards, guide other senior engineers, and remain deeply hands-on in building mission-critical systems and features. You will define and enforce backend best practices across the Software Development Lifecycle (SDLC), ensuring high-quality code, consistent patterns, and reliable delivery. Your technical judgment and code quality will directly influence the platforms long-term scalability, reliability, and success as we expand across hundreds of thousands of users.

Key Responsibilities

1. Feature Development & APIs

• Lead the architecture and implementation of new product features, owning backend design from scoping to production.
• Build robust, scalable REST APIs and backend services supporting student workflows, faculty tools, and AI-driven features.
• Collaborate with product, design, frontend (React/Next.js), and ML teams to ensure clean data flows and well-defined service interactions.
• Drive end-to-end delivery with strong performance, reliability, and maintainability.
  

2. Backend Architecture & Distributed Systems

• Build backend services using

  Node.js

  and working familiarity with Python (FastAPI) optimized for concurrency, throughput, and fault tolerance.
• Develop asynchronous pipelines for video ingestion, media preprocessing (encoding, chunking), and multimodal LLM inference orchestration.
• Architect messaging infrastructure (Kafka, RabbitMQ, Redis Streams, AWS SQS, Celery) for decoupled, resilient task execution.
• Design and maintain scalable database (OLTP) schemas for high-throughput workloads (uploads, job states, inference outputs).
  

3. Infrastructure, Cloud & Operations

• Own the backend deployment lifecycle on AWS.
• Build and operate serverless container workloads on AWS ECS + Fargate, including designing task definitions and managing services.
• Configure autoscaling policies, optimize capacity provider strategies, and ensure production-grade reliability for all ECS/Fargate deployments.
• Collaborate with DevOps to run and scale services on Kubernetes (HPA/VPA tuning, rollout strategies, cluster efficiency).
• Ensure robust observability across services including metrics, logs, and traces using tools such as Prometheus, Loki, Grafana, Tempo, and OpenTelemetry.
• Familiarity with integration for product and operational analytics tools like PostHog, Amplitude, or Mixpanel.
  

4. Technical Leadership

• Lead architectural discussions, code reviews, and design decisions across backend systems.
• Mentor and guide engineers on technical excellence, setting engineering standards and best practices.
• Identify and address performance bottlenecks, scaling challenges, and operational risks.
• Contribute to architectural standards using principles like CQRS, event sourcing, or domain-driven design.

Required Skills

• Advanced expertise in

  Node.js and Next.js

  for backend/API development (server actions, route handlers, backend integration patterns).
• Deep hands-on experience with

  AWS ECS and Fargate

  for production serverless container workloads.
• Familiarity with

  Kubernetes orchestration

  : autoscaling, rollout strategies, pod/network tuning, node pool management.
• Strong experience with message queues and async task execution: Kafka, RabbitMQ, Redis Streams, SQS, or Celery.
• Strong command of relational (e.g., PostgreSQL) and NoSQL/Caching (e.g., MongoDB, Redis) including modeling, indexing, tuning and partitioning.
• Experience with distributed caching, rate limiting, circuit breakers, and backpressure strategies.
• Experience with chunked/resumable uploads and large-object handling.
• Expert-level systems design and architecture skills

  ,

  including deep knowledge of trade-offs in distributed computing environments.

Bonus Skills

• Experience working with multimodal LLM pipelines or GPU/CPU-based inference workloads.
• Familiarity with workflow orchestration tools ( eg., Airflow, Temporal, Argo Workflows) and how they integrate into microservice architecture.
• Conceptual understanding of vector databases (e.g., Pinecone, Weaviate, FAISS) and embedding pipelines.