Full-Stack Developer

Description At Sequoia Connect , we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the global digital landscape. We move beyond traditional models to act as a catalyst for the top 1% of global talent, connecting human potential with complex industrial execution. By joining our inner circle, you are not simply taking a position; you are aligning with a strategic partner dedicated to updating your "Human OS" and accelerating your growth through world-class, high-impact projects. We are currently partnering with a rapidly growing, automation-led powerhouse that serves 31 Fortune 500 companies across the financial, healthcare, and manufacturing sectors. With a global workforce of over 32,000 employees and a presence in 28 countries, our client is a titan of digital transformation. Their "Automate Everything, Cloudify Everything" strategy ensures you will be working at the absolute forefront of AI-driven automation and cloud solutions. This is your chance to thrive in a "Customer Success, First and Always" environment that prizes continuous learning and radical ownership. You will collaborate within an international network of expertise across 39 delivery centers worldwide, gaining exposure to complex engineering challenges that redefine industrial standards. If you are a driven professional looking for a dynamic, forward-thinking workplace where your growth is the priority, this is where you belong. We are currently searching for a Full-Stack Developer : Responsibilities Build end-to-end products, taking full ownership from backend API architecture to frontend UI development. Develop, integrate, and maintain production-grade machine learning pipelines (ML) , with a specific focus on Natural Language Processing (NLP) applications. Design and implement fast, reliable, and scalable model inference APIs. Manage data processing workflows to ensure optimal performance of ML models in production. Deploy applications using AWS or cloud-agnostic methodologies, managing containerization and continuous integration/continuous deployment. Requirements Core Development: Strong expertise in building user interfaces with React and developing backend APIs using Python and FastAPI. AI/ML Integration: Proven expertise working with Python-based Machine Learning (ML) pipelines, especially Natural Language Processing (NLP). Infrastructure & Deployment: Hands-on experience with AWS or cloud-agnostic deployment (e.g., ECS/Fargate, Docker). DevOps: Solid experience setting up and managing CI/CD pipelines. Architecture: Strong understanding of data processing, scalable architecture, and exposing model inference APIs. High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery. Technologist DNA: A deep understanding of the difference between "coding" and "engineering" (knowing how to productionize and scale an ML model, not just run it in a notebook). Desired Familiarity with cloud-native foundations or AI coding assistants. Languages Advanced Oral English. Advanced Spanish. Note: Fully remote. If you meet these qualifications and are pursuing new challenges, start your application on our website to join an award-winning employer. Explore all our job openings | Sequoia Career’s Page : https://www.sequoia-connect.com/careers/ Requirements Core Development: Strong expertise in building user interfaces with React and developing backend APIs using Python and FastAPI. AI/ML Integration: Proven expertise working with Python-based Machine Learning (ML) pipelines, especially Natural Language Processing (NLP). Infrastructure & Deployment: Hands-on experience with AWS or cloud-agnostic deployment (e.g., ECS/Fargate, Docker). DevOps: Solid experience setting up and managing CI/CD pipelines. Architecture: Strong understanding of data processing, scalable architecture, and exposing model inference APIs. High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery. Technologist DNA: A deep understanding of the difference between "coding" and "engineering" (knowing how to productionize and scale an ML model, not just run it in a notebook).