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Full Stack Engineer

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Weekday AI
Full Time position
Listed on 2026-01-07
Job specializations:
  • Software Development
    Software Engineer, Cloud Engineer - Software, AI Engineer, Full Stack Developer
Salary/Wage Range or Industry Benchmark: 180000 - 250000 USD Yearly USD 180000.00 250000.00 YEAR
Job Description & How to Apply Below

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, Type Script , 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.

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 Type Script (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 Graph

    QL 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 Type Script for frontend applications.
  • Frameworks: Hands-on experience with Python frameworks (FastAPI, Flask, Django) and Type Script-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 No

    SQL databases (Postgre

    SQL, MySQL, Mongo

    DB, or similar).
  • AI/ML Integration: Experience integrating AI/ML models into production workflows, preferably with Tensor Flow, PyTorch, or scikit-learn.
  • Cloud & Dev Ops: Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines, along with cloud services (AWS Lambda, S3, GCP Big Query, 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.
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