We're building an internal AI system that runs entirely on our own hardware (no cloud inference) against our own company data. We have a working proof-of-concept and want to get the architecture right. We need an experienced consultant to review what we've built, pressure-test our decisions, and tell us where we're wrong. This is an advisory/validation role first — we have someone doing the hands-on work; what we want is a senior second opinion to make sure we're building this the right way. What we're running today: Inference: RTX 5090 (32GB, Blackwell), Ubuntu 24.04, running llama-server (llama.cpp + CUDA) serving Gemma 4 31B-it (Q4_K_M GGUF) at a 262,144 context window. Also hosts our MCP retrieval server, PostgreSQL, and Qdrant. Embeddings: separate machine with an RTX 3060 running vLLM serving Qwen3-Embedding-4B. RAG: hybrid retrieval — Postgres full-text search + Qdrant semantic search with RRF fusion, exposed through a custom MCP server with tool-calling. Data: ingesting our own internal operational data into Postgres + Qdrant. Planned stack: LiteLLM for model routing, n8n for automation, Open WebUI for the interface, Langfuse for observability, Vault or Infisical for secrets, Keycloak/Azure AD for SSO. What we need help with: Validating our two-machine split (inference vs. embeddings) and whether our VRAM/context budget holds up under real load — specifically whether a 256K context window is real and performant on a single 32GB card or just nominal. Model selection and routing strategy: which open-weight models for which tasks, and how to structure LiteLLM routes. RAG quality: chunking, embedding dimensionality, hybrid search tuning, reranking — making retrieval actually accurate on messy real-world data. Sanity-checking our overall architecture and telling us our blind spots. You should have done: Stood up local LLM inference in production — llama.cpp/llama-server and vLLM, not just Ollama on a laptop. You understand GGUF quantization (Q4_K_M, IQ-series), KV cache, KV-cache quantization, and how context length maps to actual VRAM consumption. Real fluency in GPU sizing math — given a model, a quant, and a context window, you can tell us whether it fits on a given card and what throughput to expect. Bonus if you've worked with Blackwell / sm_120a. Built production RAG — vector DBs (Qdrant, pgvector), hybrid search, RRF fusion, embedding model selection, reranking, evaluation. Worked with agentic/tool-calling systems and ideally MCP servers. Know the open-weight model landscape (Gemma, Qwen, Llama, Mistral, Phi, Nemotron, Hermes) and their licenses well enough to advise. Production ops: systemd, Docker, model gateways (LiteLLM or similar), observability (Langfuse), secrets management, SSO.

Summary We're an early-stage AI startup building Hirey — an agent-to-agent marketplace that runs inside various AI tools via a plugin. Think "Upwork for AI agents": your agent finds, vets, and books the right human or agent on your behalf. We're looking for 5 AI agent enthusiasts to install our plugin (OpenClaw, Codex, Opus, Gemini), try it out, and sit for a short 10-15 minute video interview about your experience. The interview will be posted on our hirey.ai site. About 45 minutes of your time total. What you'll do Install the Hirey plugin in Codex. It connects your agent to Hirey's remote MCP server, so there's no local server, Node setup, Claude Desktop, or JSON config edit required. Setup is usually: enable the plugin, restart the AI agent you installed on. Connect to Hirey, run a sample workflow, and check out the hirey.ai page. A 10-15 minute video interview with the founding team. We'll ask about your experience with Hirey and your broader take on the AI agent/MCP ecosystem. Camera on, recorded, and published on hirey.ai — by taking part you're agreeing to be filmed and featured on our site. Who we're looking for Someone who has used AI tools in the past, especially for coding or technical tasks. You use Claude Desktop, Cursor, Codex, or similar AI dev tools regularly. Bonus: you've built or contributed to anything in the AI agent / MCP / LangChain / Claude Code ecosystem. What you get $20 flat, released via Upwork on interview completion. A feature on hirey.ai as an early voice in the AI agent space. Early access to the Hirey AI agent network if you want to keep using it. A direct line to the founding team. To apply, answer these in your proposal Have you used an AI coding tool before? Which one(s)? One sentence on a recent AI/agent project you've worked on or played with. Your timezone and earliest availability this week. Confirm you're comfortable being filmed and featured on hirey.ai. We'll respond within 24 hours and schedule interviews within 2 business days. No long applications, no portfolio review. Optimizing for speed.

I am looking to partner with a competent dev team (individual or team) to launch AI implementation solutions for small and medium-sized businesses. As a sales professional, I aim to provide innovative solutions that enhance business operations. The ideal candidate will have experience in AI technologies and a strong understanding of business needs.

Overview We’re looking for an experienced AI engineer or AI systems builder to help us design and build an internal intelligence layer that turns fragmented customer data into actionable growth opportunities. Right now, customer insights live across multiple disconnected systems — CRM notes, product usage data, emails, support tickets, and spreadsheets. While the data exists, it is not structured in a way that helps us proactively identify expansion opportunities, churn risks, or account-level next steps. We want to build an AI-driven system that continuously synthesizes this information and helps our team understand: * What is happening inside each account * Where expansion or upsell opportunities exist * Which accounts are at risk and why * What the next best action should be for each customer ⸻ What You’ll Build You will design and implement an AI system that can: * Ingest structured and unstructured data (CRM, emails, notes, product signals) * Build dynamic “account intelligence profiles” for each customer * Identify patterns across accounts (usage drops, feature gaps, expansion signals) * Generate clear, human-readable account summaries * Recommend next-best-actions for sales, customer success, or leadership * Surface expansion opportunities based on behavioral and contextual signals * Flag risk signals early with supporting reasoning ⸻ Ideal Output For each account, the system should be able to generate: * A concise account narrative (“what’s going on here”) * Key signals and anomalies * Expansion opportunities (with rationale) * Risk factors (churn or stagnation indicators) * Suggested actions for the team this week * Confidence level and supporting evidence ⸻ Why This Matters We are sitting on a large amount of customer data, but most of it is passive. The goal is to turn it into an active intelligence system that helps our team: * Prioritize the right accounts * Increase expansion revenue * Reduce churn risk * Spend time on the highest-impact opportunities This becomes a core internal system that directly impacts revenue efficiency and customer outcomes. ⸻ Ideal Candidate We’re looking for someone with experience in: * LLM-based systems and agentic workflows * Data pipelines and multi-source data ingestion * Prompt engineering + structured reasoning systems * CRM systems (Salesforce, HubSpot, etc.) * Customer analytics / product analytics * Building internal AI tools or copilots * Backend + API integration work Bonus if you’ve worked on: * RevOps tooling * Customer success platforms * Data enrichment or account intelligence systems * SaaS growth analytics ⸻ Deliverables * System architecture for AI customer intelligence layer * Data ingestion and normalization approach * Prompting / reasoning framework for account analysis * Prototype system (or working MVP) * Output format for account intelligence reports * Documentation for internal expansion and scaling * Recommendations for tooling (build vs buy decisions) ⸻ Engagement This starts as a project-based build, but could expand into a long-term role as we scale the system across our entire customer base and additional workflows. ⸻ To Apply Please include: * Examples of AI systems or agentic workflows you’ve built * Experience integrating LLMs with real business data * Your recommended architecture for a system like this * Any clarifying questions you’d want answered before starting

I’m running a real estate investment platform called ToInvested.com. The project is about 90% finished, and most of the code was built with Claude together with another engineer. Now I need a senior engineer to step in, review the full product carefully, test every major workflow, and help verify that everything is working correctly before it goes live. This is not just a “write more code” role. I need someone who can look at the platform like a real product, find hidden bugs, catch weak logic, test edge cases, review the AI-generated code, and tell me honestly what is ready and what still needs fixing. Because this is a real estate investment platform, accuracy and trust matter a lot. Users may rely on property data, investment logic, calculations, and AI-driven insights, so even small issues can create a serious problem later. The ideal person has strong full-stack experience, understands AI-assisted development, and has a good testing mindset. Real estate tech experience would be a big plus, especially with property platforms, investment tools, marketplaces, mortgage systems, or financial workflows. My main goal is simple: I want someone to break the project before real users do. If you’re the kind of engineer who can take a nearly finished product, test it deeply, clean up weak areas, and help make it production-ready, I’d be happy to talk.

We're looking for a senior backend engineer to own the server side and cloud infrastructure for a secure healthcare mobile app. This is the person who stands up our AWS environment correctly on day one, moves us off a single VPS, and closes our HIPAA gap. The app serves healthcare field representatives who need secure workflows for managing documents, profile information, status visibility, and access-related functionality. You'll own the API, the cloud migration, and the security foundation the rest of the product depends on. What you'll own: - Backend/API: Python + async FastAPI, PostgreSQL + SQLAlchemy, Redis + background jobs, keeping business logic in the API (thin-client pattern) - AWS/DevOps: migrating off a single VPS to AWS (ECS Fargate, RDS, ElastiCache, S3), infrastructure-as-code (Terraform or CDK), CI/CD, secrets management, observability - A well-architected AWS landing zone with separate non-prod and prod environments (no real customer/PII data in non-prod) - Amazon Bedrock migration so document parsing is HIPAA-covered - Security: secure document handling, auth/session workflows, least-privilege IAM, encryption, audit logging - Building with SOC 2 in mind from day one Must have: - Python with async web frameworks (FastAPI strongly preferred) - PostgreSQL and an ORM with real migration experience (SQLAlchemy a plus) - Hands-on AWS: ECS/Fargate or equivalent, RDS, S3, IAM, Secrets Manager - Infrastructure-as-code (Terraform or CDK) and CI/CD (GitHub Actions) - Security fundamentals: token auth, secrets handling, least-privilege IAM Strongly preferred: - Redis / background job queues - HIPAA or other regulated-data experience (PHI, encryption at rest/in flight, audit logging) - Amazon Bedrock or other LLM-API integration experience - Docker / containerization

Conversational AI / LLM Consultant We are looking for a Conversational AI and LLM specialist to support the strategy, design, development, testing, and improvement of AI-powered chatbot and voice automation solutions across multiple business groups. Responsibilities: Help identify, evaluate, and prioritize Conversational AI and LLM use cases across defined business units. Advise on best practices for Conversational AI strategy, LLM architecture, prompt design, orchestration, retrieval, integrations, and development. Recommend improvements across AWS services, Amazon Lex integrations, LLM workflows, and supporting AI infrastructure. Collaborate with the development team on chatbot, voice bot, Lex, and LLM-based implementations and configurations. Conduct QA testing to validate Conversational AI functionality, accuracy, performance, reliability, and user experience. Support the development of solution frameworks, automation workflows, dashboards, application management tools, and fulfillment processes. Assist in designing and extending multilingual Conversational AI solutions in English and Spanish. Support multiple lines of business, call flows, customer journeys, and AI-assisted workflows. Ideal Candidate: Experience with Conversational AI, LLMs, and chatbot or voice automation systems. Familiarity with Amazon Lex and AWS AI services is helpful, but broader LLM architecture experience is equally important. Strong understanding of prompt engineering, AI orchestration, integrations, QA testing, and production AI workflows. Ability to translate business requirements into practical AI-driven solutions. Experience with multilingual conversational design, especially English and Spanish, is a plus.

We're building a confidential, AI-native operating system for high-volume plaintiff-side litigation. This is not a generic legal chatbot. It's an operating system for litigation operations — and we already have a live law firm as the proving ground, a working visual prototype, a pitch deck, and a near-term demo deadline. We need a senior full-stack engineer who can take an existing prototype, schemas, prompts, and workflow package and turn it into a secure working demo, then a production-track MVP. The right person thinks like a product architect, engineer, and security operator at once — fast, but disciplined with confidential legal data. Required: React/Next.js, TypeScript, Node or Python/FastAPI, PostgreSQL, auth and role-based access control, OpenAI or comparable LLM APIs, structured JSON/schema outputs, secure file handling, PDF/export generation, GitHub workflows, and strong security discipline. Strong plus: Legaltech, plaintiff-side litigation, case management systems (Filevine, Litify, Clio, Salesforce, HighLevel), RAG/document extraction, audit logging, and SOC 2 / PII / regulated-data experience. Ground rules: NDA required. No public repos. No real client data in the demo — sanitized data only. No API keys in browser code. No external sharing or deployment without approval. First deliverable: A build-readiness report identifying what's mock, what's reusable, and what needs rebuilding, plus architecture, security risks, database plan, API integration path, and a 7–30 day build roadmap. The path: Paid 7-day build-readiness sprint → 30-day demo sprint → longer-term technical lead / founding engineer discussion. To apply, please include: A short note on why you're right for this project 2–3 relevant products you've built (links) GitHub or code samples, if available Your availability for a 7-day build-readiness sprint Your hourly rate, fixed sprint price, or contract-to-hire preference Remote acceptable. U.S.-based preferred; South Florida a plus.

We are looking for a skilled Full-Stack Developer with experience in AI and software engineering to support client communication and technical requirement gathering. In this role, you will be responsible for engaging directly with stakeholders through calls, understanding their technical and business needs, and accurately translating those requirements into clear, actionable documentation for the development team. The ideal candidate should have a strong technical background in full-stack development, AI systems, and modern software architecture, enabling them to ask the right questions, validate assumptions, and ensure that project requirements are correctly captured without ambiguity. You will act as a bridge between clients and engineering teams, ensuring smooth communication and reducing misunderstandings during project execution. Responsibilities include conducting requirement-gathering calls, clarifying technical specifications, documenting system needs, and collaborating closely with developers and product teams. Strong understanding of AI-driven systems, web technologies, and software development workflows is essential to perform effectively in this role. We are seeking someone who can combine technical expertise with clear communication skills, ensuring that complex requirements are translated into structured, developer-ready specifications that support successful project delivery.

We're hiring 2 experienced developers to build AIOS deployments for our growing client base. FluentOS is a 9-person team that builds AIOS (AI operating systems) that connect a business's existing platforms together and run AI agents on top of them. We're onboarding 6–7 new client projects every week and need two more builders who can take a project and deliver it. The industries we work in: We deploy across a wide range of professional and service businesses, so you'll get variety: - Financial services & wealth advisory - Tax & accounting firms - Dental and medical practices - Home services & roofing - Property management Each client runs on a different stack — CRMs, comms tools, scheduling, payment systems, document and data sources — and our job is to unify those into one system and build agents that operate across them (lead response, follow-up, reporting, document workflows, estimating, intake, and more). What you'd be building: - AIOS deployments end to end — integrating client platforms via their APIs - AI agents that read/write across those connected systems - Reliable, production-grade automations that real businesses depend on daily What we're looking for: - Proven delivery experience — you've shipped projects clients actually use, not just personal experiments - Strong coding background (Python and/or TypeScript); comfortable with API integrations and agent frameworks - Experience with LLM/agent development (Anthropic/Claude, tool use, multi-step agents) is a big plus - Fast, organized, and communicative — we move quickly and build as a team, not in isolation - Able to work closely with Ray, our lead developer, who'll get you ramped into live projects The setup: - 1099 contract, paid per project — not salaried - Steady, scalable volume (6–7 new projects/week) means consistent work for builders who deliver - Strong potential for ongoing, long-term collaboration as we scale To apply: Tell us briefly about a real project you've built and delivered — ideally something involving API integrations, automation, or AI agents. Include links to work or repos if you have them. Please start your reply with the word "FLUENT" so we know you read this. We'll be scheduling interviews shortly.