We are looking for a senior, enterprise-grade Software Engineer to build a high-performance Proof of Concept (POC) for an Autonomous B2B Insurance Compliance & Asset Monitoring Agent. The system will bridge the gap between complex commercial insurance policy guidelines (unstructured data via RAG) and real-time operational telemetry (structured IoT log streams in Snowflake). The goal is an intelligent agent that dynamically analyzes asset anomalies against policy definitions to flag compliance breaches instantly. This is a highly intensive 1-week, 50-hour sprint for an engineer who understands complex data schemas, time-series IoT data gravity, and deterministic agent routing. Core Architecture Requirements (What You Will Build) The Multi-Agent Orchestration Layer: Build an autonomous agent framework (using LangGraph, AutoGen, or native Python execution loops) capable of multi-step reasoning, tool-calling, and error self-correction. Snowflake & IoT Data Lake Integration: Grant the agent secure access to a Snowflake environment containing structured IoT asset monitoring data (e.g., machine telematics, temperature logs, usage hours, maintenance schedules). The agent must dynamically discover schemas and write optimized SQL queries to aggregate this data. Insurance Compliance Hybrid RAG Pipeline: Implement a semantic search architecture to parse and embed dense, unstructured insurance underwriting guidelines, warranty contracts, and B2B compliance policies into a vector store. Deterministic Tool Use & Evaluation: The agent must safely look up a specific policy compliance rule (via RAG) and then automatically generate and execute a targeted query against the Snowflake IoT data lake to verify if real-world asset telemetry violates that specific rule. Required Technical Stack & Expertise Languages & Core Frameworks: Python (FastAPI / Typer), LangChain / LangGraph, LlamaIndex. AI & Embeddings: OpenAI GPT-4o / Anthropic Claude 3.5 Sonnet APIs, vector databases (Pinecone, pgvector, or Snowflake Cortex search). Data Architecture: Deep production experience with Snowflake, cloud data lakes, time-series IoT data manipulation, and advanced text-to-SQL generation patterns. Observability: Implementation of tracing tools like LangSmith, Phoenix, or Arize to monitor prompt tokens, routing paths, and costs. Project Timeline & Deliverables (50 Hours) Phase 1 (Hours 1–15): Environment setup, ingestion of sample insurance policy PDFs into the vector store, and secure connection string setup to a Snowflake mock IoT dataset. Phase 2 (Hours 16–35): Core development of the multi-agent execution loop, prompt tuning for text-to-SQL generation against time-series data, and policy-to-telemetry mapping logic. Phase 3 (Hours 36–50): Observability integration, edge-case testing for hallucination mitigation (ensuring false compliance flags are eliminated), clean code delivery, and a walkthrough video demonstration. If this sprint goes smoothly, there is an immediate opportunity to extend this contract into long-term architecture maintenance, production scaling, and integration into our core platform. Please include real examples of multi-agent, IoT, or insurtech data pipelines you have personally built in your application.

Hi We are looking for someone who is expert in Replit/Lovable to for 3 week series of workshops of app building. We are looking for someone who can enjoy imparting knowledge to kids worldwide and align with our vision of giving a level playing field to all kids around the globe. We believe most kids are being shelled by their ecosystem, and we have to expose them to the world. We are looking for someone who can enjoy the process, enjoy working with young brains and can make the workshops fun learning. If this is something you can enjoy. Let's talk. Best Sam

Summary: We are building Sphere Inc., an AI-powered SaaS platform focused on the real estate industry. The product is currently in the MVP stage, and we are looking for a strong full-stack developer who can help us build, refine, and launch the first working version. The platform is designed to help real estate businesses automate daily operations, improve decision-making, and use AI agents to support workflows such as property management, lead handling, deal analysis, document processing, reporting, and business automation. This is not a basic website project. We are building a real SaaS product with a modern frontend, reliable backend, AI-powered workflows, and scalable AWS infrastructure. Current Project Status: The product vision and core direction are already defined. We are currently shaping the MVP workflows, user experience, and technical structure. At this stage, our main need is execution. We need someone who can help turn the concept into a working MVP that can be tested with real users. Some workflows are still being refined, so we are looking for a developer who can contribute both technically and practically — not just write code from fixed tickets. Current Progress & Bottlenecks We have a clear direction, but need support with - Structuring the MVP architecture - Building the frontend and backend features - Designing practical AI agent workflows - Connecting AI features with real estate data and user actions - Setting up AWS infrastructure for development and deployment - Creating a clean experience for non-technical business users - Prioritizing the most important MVP features The main bottleneck right now is moving from concept/prototype stage into a stable, usable product. We are looking for someone who has experience with - Building SaaS products from MVP to production - Full-stack development with Python, Node.js, JavaScript, and TypeScript - Frontend development using React, Next.js, or similar frameworks - Backend APIs, database design, authentication, and user roles - AI agents, LLM integrations, workflow automation, or RAG - AWS deployment, storage, monitoring, and security - Real estate platforms, CRMs, property data, document workflows, reporting, or automation tools - Writing clean, maintainable, and scalable code Tech Stack: Python, Node.js, JavaScript/TypeScript, React or Next.js, PostgreSQL, AWS, REST APIs, and AI/LLM tools such as OpenAI, Anthropic, AWS Bedrock, LangChain, LangGraph, or similar frameworks. Responsibilities: - Build frontend and backend features for the MVP - Design and implement AI-powered workflows and agent features - Connect APIs, databases, authentication, and user roles - Set up or improve AWS infrastructure - Help prioritize features and identify technical risks - Communicate progress clearly and regularly Some Knowledge That Is a Plus: - Real estate CRM, property management, brokerage, acquisitions, or leasing platform experience - AWS Bedrock, LangChain, LangGraph, or vector databases - Multi-tenant SaaS architecture - Stripe or subscription billing - DevOps, Docker, CI/CD, and testing - Analytics dashboards, reporting tools, or document automation The ideal candidate is a reliable full-stack developer who can work independently, understand the product vision, ask smart questions, and help us make practical technical decisions during the MVP stage. We need someone who is comfortable in an early-stage environment and can help turn a clear idea into a working SaaS product for real estate users. Please include: 1. Your GitHub, portfolio, or examples of previous work 2. A brief description of your related SaaS experience 3. Examples of AI agent, AI automation, or LLM-powered products you have built 4. Your AWS experience 5. Any real estate platform, CRM, data, or automation experience 6. Your availability and preferred working style We are looking for someone who can help us build the MVP now and potentially continue with us as the platform grows.