Full Stack Engineers 5

We’re hiring senior full-stack engineers

Stack: Node.js, React/Vue, Python, PHP, PostgreSQL, Redis

Required experience (6-10+ yrs):

• Scalable REST APIs & async workers

• Auth (JWT/OAuth), background job queues

• Real-time, high-traffic webhook processing

• Protocol integrations: SFTP, SMTP, SMS

• AI development: LLMs, embeddings, vector DBs, inference workflows

• SPA architecture, state mgmt, performance tuning

• Data analytics pipelines, ETL, event tracking

• Data lake systems: DuckDB, MotherDuck, Parquet

• CI/CD, GitHub workflows, automated testing

We’re looking for engineers who think in systems, own delivery end-to-end, and operate with high autonomy.

Backend Development (6-10+ years):

Node.js, Python, PHP: Proficiency in building scalable REST APIs and asynchronous workers for high-performance backend systems.

Authentication: Expertise in implementing JWT and OAuth for secure user authentication and authorization.

Background Job Queues: Experience with tools like Bull, Celery, or Sidekiq for managing async tasks.

Real-Time Webhook Processing: Handling high-traffic, real-time webhook systems for seamless integrations.

Protocol Integrations: Knowledge of SFTP, SMTP, and SMS protocols for external system connectivity.

Database Management:

PostgreSQL: Designing and optimizing relational databases for complex queries and scalability.

Redis: Using in-memory data stores for caching and real-time processing.

Data Systems:

Data Analytics Pipelines: Building ETL processes and event tracking systems for data insights.

Data Lakes: Proficiency with DuckDB, MotherDuck, and Parquet for managing large-scale data storage and analytics.

Frontend Development:

React/Vue: Building single-page applications (SPAs) with a focus on modular, maintainable code.

State Management: Expertise in tools like Redux, Vuex, or Zustand for efficient state handling.

Performance Tuning: Optimizing frontend performance for fast load times and smooth user experiences.

AI Development:

Experience with large language models (LLMs), embeddings, and vector databases (e.g., Pinecone, Weaviate).

Knowledge of inference workflows for deploying and optimizing AI models in production.

DevOps & CI/CD:

Proficiency in GitHub workflows for continuous integration and deployment.

Experience with automated testing frameworks (e.g., Jest, Cypress, Pytest) to ensure code quality.

Soft Skills & Mindset:

Systems Thinking: Ability to design and architect end-to-end solutions with scalability and reliability in mind.

End-to-End Ownership: Taking responsibility for the full development lifecycle, from planning to deployment and monitoring.

High Autonomy: Working independently with minimal supervision, proactively solving problems and driving projects to completion.

Collaboration: Effectively communicating with cross-functional teams to align on project goals and deliverables.