Join our team as a senior AI Architect working closely with our product and engineer teams to design practical AI capabilities within our SaaS platform. This is a hands-on role focused on building reliable, production-grade conversational and AI-assisted features — not experimental research projects. You will work closely with product and engineering teams to design scalable AI patterns, integrate modern LLM technologies, and help shape how AI capabilities are embedded into real operational workflows. You will focus deeply on architecture, implementation quality, reliability, usability, scalability, observability, and operational robustness. This role is ideal for someone who understands both modern AI tooling and the realities of shipping enterprise SaaS software in production environments. We value people who can think critically about architecture, tradeoffs, operational realities, and long-term maintainability — not just prototype AI demos.

We are an investment firm with a portfolio of healthcare companies. We are seeking to begin building our data capture systems across our business and layer AI to surface summarize and store insights. This is a process that is in parallel to our operations team SOP'ing our process in anticipation of expansion. It is our opinion that we have a relatively simple business process from end to end and lots of potential to capture useful data signals across each department/function. We have drafted a rough business process / data ontology diagram showing our preferred approach. We are seeking an expert to: 1 ) Create lightweight data systems to capture data signals from end to end across our business (Recruiting to Onboarding to Scheduling to Payroll to Finance to Legal to). This also includes organizing and categorizing our past / existing data in addition to capturing signals for future data. 2 ) Layer AI / agentic AI automations that can surface insights, categorize and aggregate info, populate knowledge databases, etc. Example Data Signals / Use Cases: Fireflies recorded meetings Tagging emails in inbox as Legal/Finance/Scheduling/Onboarding etc Job Board Postings Airtable (For building a lightweight scheduling/employee management system) (For storing a knowledge database and rolodex) To Apply: Please briefly present an instance of implementing a similar lightweight solution to capture data signals and convert the data into meaningful and actionable insight via AI

Forum Intelligence: Project Brief & Initial Rollout 1. Executive Summary & Objective Forum Intelligence is a beginning as a localized data retrieval, processing, and archiving system designed to scrape public municipal records and state legislative data for public oversight. The immediate objective is to build a functional, highly resilient prototype focused on the Tri-Cities region (Burbank, Glendale, and Pasadena, California). The system will autonomously ingest messy, unstructured municipal data (City Council meeting minutes, agendas, public notices, and legislative PDF text, recorded mp4), clean it, and make it fully searchable and queryable via a localized AI agentic framework. 2. Phase 1 Scope: The Tri-Cities Rollout Th engineer will be responsible for building two primary pillars: A. Resilient Scraper Bots • Target Ingestion: Monitor and pull data from Burbank, Glendale, and Pasadena municipal portals and California legislative feeds. • Data Types: Brittle HTML sites, heavily nested tables, public notices, legislative drafts, and massive unstructured PDF archives. • Requirements: The scraping architecture must be exceptionally robust, utilizing intelligent error handling, retry semantics, and pagination tracking to handle frequent municipal website layout changes without breaking the pipeline. B. Ingestion & Vector Pipeline • Parsing: Extracting clean text from poorly formatted documents and scanned PDFs. • Local RAG (Retrieval-Augmented Generation): Chunking and embedding the data locally into a vector database (e.g., pgvector, Chroma, or Milvus) to enable semantically accurate entity linking and contextual search. 3. Targeted Hardware Stack To ensure maximum data security, strict public oversight integrity, and predictable operational costs, Forum Intelligence is skipping commercial cloud APIs in favor of an on-premise, localized NVIDIA enterprise deployment. The production roadmap aligns precisely with the new computing patterns detailed in NVIDIA’s latest hardware roadmap: • Inference & Token Generation: Running local open-weight frontier models (e.g., Neotron 3 Ultra or Claude/Llama equivalents) optimized for reasoning and long-context tool use. • Compute & Orchestration: The backend infrastructure is architected around NVIDIA’s dedicated agentic architecture, utilizing high-instructions-per-clock (IPC) Vera CPUs paired with Vera Rubin GPUs. • Memory & Storage Processing: Utilizing NVIDIA’s unified memory fabric and data processing units (DPUs) for ultra-low latency context management, KV caching, and fast vector database retrieval. 4. Immediate Milestones for the Engineer 1. Architecture Design: Map out the database schema and local inference ingestion loop. 2. Tri-Cities Scraper Deployment: Write and deploy the initial automated bots for Burbank, Glendale, and Pasadena. 3. Local MVP Pipeline: Demonstrate a local RAG pipeline where a user can query the Tri-Cities scraped records and receive grounded answers with exact source attributions. The above was AI generated from months long conversations with Gemini. The goal is to prove the concept then roll out to LA County, state of CA, and then the country.

Overview We're building an open-source CLI gateway for multi-agent AI orchestration — model-agnostic, MCP-native, and designed to bring any agent framework online with a single command. The repo is active, well-documented, and growing. We need an engineer to accelerate integration coverage and help attract open-source contributors. The Work Build agent templates and runnable examples for LangGraph, CrewAI, and similar frameworks Add LLM provider support (Groq, Mistral, Gemini, etc.) to the Hermes runtime Write clean, contributor-friendly code that models good PR hygiene Submit work via fork → PR → merge workflow on GitHub You Are Strong Python developer with CLI tooling experience Familiar with at least one of: LangGraph, CrewAI, LiteLLM, LangChain Comfortable with open source GitHub workflows (fork, PR, issues, reviews) Self-directed — you read docs, ask good questions, and don't wait to be unblocked Nice to Have Experience with MCP (Model Context Protocol) Familiarity with SSE, OAuth 2.1, or agent credential management Prior open source contributions Engagement Part-time to start, 20 hrs/week Fixed milestones per integration delivered Potential to grow with the project To Apply Share your GitHub profile and one example of open source work or a project that shows your Python and agent framework experience. https://github.com/ax-platform/ax-gateway

Looking for an elite problem solver, either an ex-MBB consultant with deep technical fluency or a seasoned AI Product Manager, to act as the crucial bridge between business operations and technical AI implementation. You will be responsible for dissecting client operations, prototyping the AI logic, writing flawless engineering briefs, and driving the development team to the finish line. What You Will Do: Workflow Audits & Discovery: Lead deep-dive discovery sessions. Map out existing operational workflows, identify bottlenecks, and pinpoint high-ROI opportunities for AI automation. Prototyping & Technical Translation: Because you are an AI power user, you will prototype the initial AI prompts and logic. You will then translate your operational findings into crystal-clear engineering briefs (or PRDs) so developers know exactly what to build. Development Management: Act as the project lead during execution. Manage the engineering workstream, clear roadblocks, and ensure the final solution is delivered on time, within budget, and achieves the strategic goal. Stakeholder Alignment: Act as the primary liaison between non-technical business leaders and the technical development team. Requirements & Qualifications: Top-Tier Pedigree: 1+ years of experience at a top management consulting firm (MBB, Big 4, Tier 2) OR proven experience as a Technical/AI Product Manager. Advanced AI Fluency: You are an AI power user. You possess advanced prompt engineering skills (e.g., chain-of-thought, few-shot) and know how to force LLMs to output reliable, structured data. Elite Structured Thinking: You excel at turning highly ambiguous, messy business processes into clean, logical frameworks (using Lucidchart, Miro, etc.) and comprehensive technical requirements. Project Leadership: Proven track record of managing technical resources, tracking deliverables, managing budgets, and driving teams to a deadline.

I'm seeking an expert to develop an AI agent that will assist in managing my business. The AI should be able to handle tasks such as scheduling, data analysis, and customer communication. The ideal candidate will have experience in business management and AI development, and be able to deliver a functional prototype within a month.

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.

We are seeking a hands-on AI systems expert to help us establish, secure, and scale our internal AI capability. The role involves both technical implementation and advisory responsibilities, with the expectation of staying on as a trusted advisor. The ideal candidate will have a strong background in AI systems and be able to provide strategic guidance.

I’m looking for an experienced developer who can help me set up a real-time AI avatar similar to the attached Tomcat reference videos. My goal is to create a Barbie-style AI avatar that can livestream through TikTok Studio. I want the character to be able to speak, animate naturally, and interact during live streams. My current setup: * MacBook Pro * iPhone I’m looking for someone who has successfully built a setup like this before and can walk me through the entire process step by step, helping me get everything running on my own MacBook Pro so I can manage it myself afterward. Budget: Negotiable. At this time, I’m not able to purchase additional physical hardware, so I’m looking for the best setup possible using only my MacBook Pro and iPhone.

We are seeking a skilled GenAI engineer to work with our client in a remote or Chicago-based capacity. The ideal candidate will have experience in developing and implementing AI solutions, with a strong understanding of machine learning and data analysis. Responsibilities include designing AI models, integrating AI into existing systems, and collaborating with cross-functional teams to enhance AI capabilities. If you have a passion for AI and a proven track record in delivering innovative solutions, we would love to hear from you.