Full Stack Developer - Python/Flask/FastAPI & LLM & React
Technology
RLink Solutions LLC
San Francisco, United States$14,733 - $15,600 /month1 month ago
Full timeOn-site
Job description
Full Stack Engineer
SFO, CA- Hybrid (Tuesday to Thursday in office)6+ months with high possibility of extension
Job Description:
Key Responsibilities:
- System Architecture & Database Design: Architect scalable, low-latency backend systems capable of handling massive datasets and live risk-data updates. Design highly optimized database schemas (SQL/NoSQL) and data orchestration pipelines tailored for FRM automation.
- API & Service Orchestration: Develop and manage RESTful APIs, GraphQL, and high-performance real-time communication layers (WebSockets/gRPC-web). Implement microservices architecture and Model Context Protocols (MCPs) to securely connect AI agents with internal enterprise data.
- Advanced AI & LLM Integration: Design, integrate, and deploy production-ready LLM systems using providers like OpenAI and Anthropic. Build sophisticated backend workflows using AI agent frameworks (Lang Graph, CrewAI), function calling, and complex tool-use patterns to automate business logic.
- Full-Stack Execution & Frontend Delivery: Translate high-fidelity Figma designs or rapid prototypes into pixel-perfect, scalable, and secure React layouts. Manage complex asynchronous workflows using Redux + Sagas or Zustand, while implementing TanStack Query for modern server-state synchronization.
- Performance & AI Observability: Build resilient AI systems by implementing AI Observability (e.g., LangSmith, Opik), hallucination detection, and retry logic. Optimize end-to-end performance to ensure frequent real-time updates do not trigger unnecessary UI re-renders, maintaining a fluid user experience even under high data loads.
- AI-Driven Development: Lead by example in using AI-augmented coding workflows, leveraging tools like Cursor, Codex, and GitHub Copilot to accelerate cross-stack feature delivery, refactor legacy logic, and eliminate boilerplate.
You will own the system architecture for our FRM compliance and automation tools. This means moving fluidly across the entire stack-designing databases, building robust microservices, integrating Model Context Protocols (MCPs) for AI agents, and hardening interactive dashboards to handle live risk-data updates with ultra-low latency. You are a high-velocity engineer who embraces the future of software development, treating data, APIs, and interfaces as a single cohesive product.
Required Qualifications
- 7+ Years of Engineering Experience: A deep background in system design, database architecture, and scaling enterprise-grade backend services, with a recent pivot toward full-stack delivery and AI-augmented workflows.
- Core Stack Mastery: Deep proficiency in Python (Flask/FastAPI) and Node.js to develop performant service layers, combined with a strong command of React and TypeScript to build production-grade interfaces.
- Real-Time & Protocol Expertise: Expert-level knowledge of RESTful APIs, service orchestration, WebSockets, and gRPC-web to ensure data streams perfectly sync between backend nodes and client apps.
- State & Data Lifecycle Strategy: Expert implementation of high-throughput databases, caching layers (e.g., Redis), and modern state management strategies (TanStack Query, Zustand, or Redux) to handle heavy data mutations seamlessly.
- AI Tooling & LLM Orchestration: Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, Smolagents, DSPy) as well as advanced prompt engineering. Mastery of the Cursor code editor (indexing, composer, and chat features) to maintain elite velocity.
- Uber Ecosystem: Prior experience at Uber or deep familiarity with our internal developer ecosystem, internal data pipelines, and platform services.
- Workflow Domain: Background in building systems for anomaly detection dashboards, compliance tools, or complex workflow-driven automation platforms (e.g., Celery, Cadence, Temporal).
- Rapid Prototyping Fluency: Familiarity with design-to-code or AI prototyping sandboxes (like Lovable or v0.dev) to quickly map out internal tools before building them into permanent architecture.
- Product Mindset: Proven focus on delivering measurable user value, using stakeholder feedback for iterative improvement, and treating technical components as a product to ensure usability, quality, and adoption.
- Backend Core: Python (FastAPI, Flask), Node.js, Microservices, MCP (Model Context Protocol).
- Frontend Core: React, TypeScript, Fusion.js (Uber’s Framework).
- AI & LLM Tools: Cursor, GitHub Copilot, LangGraph, CrewAI, LangSmith/Opik, Lovable, v0.dev.
- State & Data: Redux + Sagas, TanStack Query, Zustand, Context API, WebSockets, gRPC-web, GraphQL, Redis, SQL/NoSQL.
- Design & UI: BaseUI design system, Styletron (CSS-in-JS), Figma, Tailwind CSS.
Keywords
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