# Full Stack Engineer

**Company:** [Weekday AI](http://jobs.workable.com/companies/pxG9rDgnvZm2c86JUchT1j.md)
**Location:** San Francisco, United States
**Workplace:** on site
**Employment type:** Full-time
**Department:** Weekday's Partner Client

[Apply for this job](http://jobs.workable.com/view/62e7f780-2b47-4f3c-ab2f-ad02dd8028ed)

## Description

**This role is for one of the Weekday's clients**

**Salary range:** $180k - $250k**Experience: 6+** YoE  
  
We are seeking an experienced **Full Stack Engineer** with a strong background in **AI-driven application development**, **Python**, **TypeScript**, and **ETL pipelines**. The ideal candidate will bring deep technical expertise across the stack, from designing and implementing scalable backend systems to creating intuitive and responsive frontends. You will play a key role in building, integrating, and optimizing systems that process and transform large datasets into actionable insights, leveraging AI technologies to deliver intelligent features.

## Requirements

### **Key Responsibilities**

-   **End-to-End Development:** Design, develop, and deploy high-quality AI-driven web applications, ensuring performance, scalability, and maintainability across both frontend and backend.
-   **Backend Engineering:** Build robust server-side applications using **Python** (FastAPI, Flask, or Django), integrating with databases, APIs, and AI/ML models.
-   **Frontend Development:** Create engaging and responsive user interfaces with **TypeScript** (React.js, Next.js, or Angular), ensuring an exceptional user experience.
-   **AI Integration:** Collaborate with data scientists to integrate machine learning models into production systems, ensuring seamless data flow and model performance monitoring.
-   **ETL Pipeline Development:** Design, implement, and optimize **ETL processes** to efficiently collect, transform, and load large-scale datasets from multiple sources.
-   **API Design & Integration:** Develop and consume RESTful and GraphQL APIs for communication between services and external systems.
-   **Cloud Deployment:** Work with AWS, Azure, or GCP to deploy, scale, and monitor applications and ETL pipelines in production environments.
-   **Testing & Quality Assurance:** Implement automated testing strategies (unit, integration, and end-to-end) to maintain software quality and reliability.
-   **Collaboration:** Work closely with cross-functional teams including data engineering, AI research, product management, and UX design to deliver impactful features.
-   **Continuous Improvement:** Stay current with emerging technologies, frameworks, and AI trends to propose innovative solutions and improve development practices.

### **Required Skills & Qualifications**

-   **Experience:** 6–10 years in full stack development, with at least 2–3 years in AI-driven application projects.
-   **Programming Expertise:** Strong proficiency in **Python** for backend/API development and **TypeScript** for frontend applications.
-   **Frameworks:** Hands-on experience with Python frameworks (FastAPI, Flask, Django) and TypeScript-based frontend frameworks (React, Angular, or Next.js).
-   **ETL & Data Engineering:** Proven track record in designing and maintaining **ETL pipelines** and working with data processing tools (Airflow, dbt, or similar).
-   **Databases:** Proficiency in relational and NoSQL databases (PostgreSQL, MySQL, MongoDB, or similar).
-   **AI/ML Integration:** Experience integrating AI/ML models into production workflows, preferably with TensorFlow, PyTorch, or scikit-learn.
-   **Cloud & DevOps:** Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines, along with cloud services (AWS Lambda, S3, GCP BigQuery, Azure Data Factory).
-   **Problem Solving:** Strong analytical and troubleshooting skills, with the ability to address technical challenges in complex systems.
-   **Collaboration:** Excellent communication skills and a track record of working in agile, cross-functional teams.

### **Preferred Qualifications**

-   Prior experience with real-time data processing and streaming technologies (Kafka, Spark).
-   Knowledge of MLOps best practices for model deployment and monitoring.
-   Understanding of data governance, security, and compliance standards.