Qode
Full-Stack Developer
Qode - South San Francisco, California, us, 94083
Work at Qode
Overview
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Overview
Job Opening: Full-Stack Developer GenAI & LLM Applications
Location: South San Francisco (SSF) Start Date: Immediate About the Role: We are seeking a hands-on Full-Stack Developer with Generative AI expertise to join our team and lead the development of a real-time AI application leveraging AWS serverless architecture, FastAPI, and LLM-based pipelines. The ideal candidate is a builder with a deep understanding of full-stack development, cloud-native applications, and AI-powered user experiences. Your Responsibilities: Build and enhance React-based frontends hosted on CloudFront Design scalable, low-latency APIs using FastAPI (Python) integrated with API Gateway (REST + WebSocket) Develop AWS Lambda functions for backend services, data handling, and orchestration Hands-on experience with OpenSearch for implementing scalable search functionality Manage authentication using SSO, and enable secure access flows Integrate real-time WebSocket interfaces for LLM streaming and dashboarding Work closely with data science teams to connect LLM pipelines (LangChain + RAG) and vector search mechanisms Design and maintain serverless data layers using DynamoDB, Aurora PostgreSQL, Athena, and S3 Participate in CI/CD automation using GitHub Actions, CloudFormation/CDK, and manage IAM roles/policies Collaborate on data pipelines using AWS Glue, Airflow (MWAA), and Sagemaker for model training/inference Key Technologies: Frontend: React, TypeScript, CloudFront Backend: FastAPI (Python), AWS Lambda, API Gateway (REST + WebSocket), Cognito Search & Storage: OpenSearch, DynamoDB, Aurora PostgreSQL, S3, Athena GenAI & RAG: LangChain, FAISS, Pinecone, OpenAI, Claude, AWS Bedrock Data Engineering: AWS Glue, Airflow (MWAA) DevOps: GitHub Actions, CloudFormation/CDK, IAM Roles Streaming & Realtime: WebSockets, LLM streaming pipelines You Should Have: Proven experience in building production-grade GenAI features using LLM APIs and RAG pipelines Hands-on experience with serverless app development on AWS Solid understanding of FastAPI, microservices architecture, and REST/WebSocket API design Comfort with both structured data (PostgreSQL) and NoSQL (DynamoDB) Experience working in a CI/CD DevOps environment with infrastructure-as-code Excellent problem-solving skills, and ability to troubleshoot complex cloud-native systems
Location: South San Francisco (SSF) Start Date: Immediate About the Role: We are seeking a hands-on Full-Stack Developer with Generative AI expertise to join our team and lead the development of a real-time AI application leveraging AWS serverless architecture, FastAPI, and LLM-based pipelines. The ideal candidate is a builder with a deep understanding of full-stack development, cloud-native applications, and AI-powered user experiences. Your Responsibilities: Build and enhance React-based frontends hosted on CloudFront Design scalable, low-latency APIs using FastAPI (Python) integrated with API Gateway (REST + WebSocket) Develop AWS Lambda functions for backend services, data handling, and orchestration Hands-on experience with OpenSearch for implementing scalable search functionality Manage authentication using SSO, and enable secure access flows Integrate real-time WebSocket interfaces for LLM streaming and dashboarding Work closely with data science teams to connect LLM pipelines (LangChain + RAG) and vector search mechanisms Design and maintain serverless data layers using DynamoDB, Aurora PostgreSQL, Athena, and S3 Participate in CI/CD automation using GitHub Actions, CloudFormation/CDK, and manage IAM roles/policies Collaborate on data pipelines using AWS Glue, Airflow (MWAA), and Sagemaker for model training/inference Key Technologies: Frontend: React, TypeScript, CloudFront Backend: FastAPI (Python), AWS Lambda, API Gateway (REST + WebSocket), Cognito Search & Storage: OpenSearch, DynamoDB, Aurora PostgreSQL, S3, Athena GenAI & RAG: LangChain, FAISS, Pinecone, OpenAI, Claude, AWS Bedrock Data Engineering: AWS Glue, Airflow (MWAA) DevOps: GitHub Actions, CloudFormation/CDK, IAM Roles Streaming & Realtime: WebSockets, LLM streaming pipelines You Should Have: Proven experience in building production-grade GenAI features using LLM APIs and RAG pipelines Hands-on experience with serverless app development on AWS Solid understanding of FastAPI, microservices architecture, and REST/WebSocket API design Comfort with both structured data (PostgreSQL) and NoSQL (DynamoDB) Experience working in a CI/CD DevOps environment with infrastructure-as-code Excellent problem-solving skills, and ability to troubleshoot complex cloud-native systems