I’m looking for an AI Engineer to help build an automated red-teaming product based on open-source models. This is a short-term, hands-on project for around 2 months, with an expected commitment of about 20 hours per week. The goal is to build a specialized red-teaming engine that can generate adversarial prompts across different risk domains, severity levels, and attack strategies — then automatically run those prompts against target AI models to identify bad cases, failure patterns, and safety gaps. 🔍 What you’ll work on Build red-teaming systems on top of open-source LLMs, including fine-tuning, prompt optimization, evaluation pipelines, and model orchestration. Design automated prompt generation workflows across risk domains such as self-harm, hate, violence, sexual safety, misinformation, fraud, cyber, and other high-risk areas. Generate prompts across different harm levels, from benign edge cases to policy-borderline and clearly unsafe scenarios, while maintaining structured taxonomies and evaluation criteria. Run automated tests against target models such as Gemma, Llama, Qwen, or other open-source / closed-source models to surface jailbreak patterns, over-refusal, under-refusal, and policy inconsistencies. Build feedback loops that turn model failures into stronger red-team prompts, improved eval sets, remediation recommendations, and continuous safety testing. 🧠 What I’m looking for Hands-on experience with open-source LLMs, fine-tuning, LoRA / QLoRA, RAG, model evaluation, and LLM inference pipelines. Familiarity with AI safety, red teaming, adversarial prompting, jailbreaks, safety evals, or trust & safety systems. Ability to build end-to-end systems, including data pipelines, model serving, eval harnesses, scoring, dashboards, and automation workflows. Bonus if you’ve worked on model safety, content moderation, policy evaluation, agentic testing, or automated eval infrastructure. ⏳ Project setup Duration: around 2 months Time commitment: about 20 hours per week Format: flexible / remote-friendly Stage: early-stage build, from 0 to 1 🚀 Why this is interesting This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate, test, categorize, and learn from model failures — helping teams understand where AI models break, why they break, and how to improve them. If you enjoy working with open-source models, AI safety, red teaming, and fast 0-to-1 product building, I’d love to chat. Feel free to DM me if this sounds like you, or if you know someone who might be a good fit.

I need a advanced agentic system built with persistent memory and up to 6 agents that work together. I am building a franchised coffee shop business. there is so much data that can be pulled together and harvested from customer spending habits and also what is the highest grossing items that sell , vs the most profitable hours of the day. All that data needs to be meshed with the actual Quickbooks data and financials. All that then needs to be balanced with real world site selection for new coffee shop locations. Here is what I need: Agent 1. Pulls information Directly from clover POS automatically. Agent 2. takes Agent's 1 information and cross references with Margin Data from Quickbooks. Recommends New drinks that are both on trend AND High Margin. Agent 3 is the financial Agent. It works directly with Quickbooks. It monitors cash flow and alerts when labor percentage exceeds parameters. It also stress tests expansion and " what if" scenarios. Agent 4. the site selection agent. agent 4 monitors LoopNet, Costar, and parcel data for commercial land available. It cross references traffic count and demographics, it checks competitor coffee presence etc. Agent 5 is the capital strategist. when agent 4 finds a location, it consults with agent 3 which is connected to Quickbooks , it models out loan scenarios, cash flow impact. and helps run " what if " scenarios that it gets asked. Agent 6 is the main Orchestrator that runs everything that I would communicate soley with through Whatsapp. It connects all the agents and pulls data collectively and makes them all work together and stress tests ideas that one agent might find.

AI Developer Needed – Build Us a Marketing AI Agent We need a skilled developer to build an AI-powered Marketing Assistant for our business. **Core Tasks the Agent Will Handle:** - Appointment setting & lead qualification - Copywriting (emails, ads, social content) - Automated follow-up sequences - Lead research and CRM updates **Requirements:** - Experience with AI agent frameworks (LangChain, CrewAI, AutoGen, etc.) - Strong prompt engineering skills - Ability to integrate with our existing tools (CRM, calendar, email) - Past projects to show us – links or demos preferred **Budget:** Open to discussion based on scope **Timeline:** Looking to kick off within 1–2 weeks

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

I’m looking for a senior AI app developer who can help me build an AI-powered MVP while also guiding me through the technical decisions. This is not just a coding task. I want someone who can think through the product, recommend the right architecture, explain tradeoffs, and build the first working version. The ideal person should be comfortable with OpenAI/LLM integrations, full-stack development, database design, authentication, deployment, and startup-style MVP execution. I’d like to work with someone who can act almost like a technical partner: build the product, teach me what is being done, and help me understand how to maintain or scale it later.

AI SYSTEMS ENGINEER Agentic AI, Multi-Agent Systems & Secure AI Workflows (U.S.) Remote • United States We're building production AI systems designed for enterprise environments. We're looking for exceptional AI systems engineers who enjoy solving difficult systems problems – not just writing code. Our work sits at the intersection of agentic AI, software architecture, enterprise systems, governance, security, and operational intelligence. We design AI systems that improve how organizations operate while meeting the standards required for production deployment. We value engineers who think in systems, challenge assumptions, and care deeply about building technology that is reliable, understandable, secure, and useful. If you're motivated by difficult engineering problems, thoughtful architecture, and building production AI systems for enterprise organizations, we'd like to hear from you. WHAT YOU'LL HELP BUILD Examples of the types of systems we design include: - Multi-agent AI systems - Enterprise AI assistants - Secure AI workflows - Enterprise workflow automation - AI-powered knowledge systems - Human-in-the-loop decision support - Document intelligence - Retrieval-Augmented Generation (RAG) - AI memory and retrieval systems - AI evaluation and testing frameworks - Secure enterprise AI platforms - AI governance capabilities - Operational intelligence platforms TECHNICAL EXPERIENCE WE VALUE We're interested in engineers with experience in some combination of: - Python - AI Agent Development - LangGraph - LangChain - Large Language Models - API Development - Vector Databases - Software Architecture - Enterprise Systems Integration - Information Security Experience with OpenAI, Anthropic, Model Context Protocol (MCP), cloud infrastructure, workflow orchestration, observability, distributed systems, or regulated technology environments is also valuable. We do not expect expertise in every technology. We care far more about engineering judgment, systems thinking, demonstrated execution, and continuous learning than checking every technology box. THE PROBLEMS WE ENJOY SOLVING The engineers who thrive here enjoy questions like: - How should multiple AI agents coordinate work? - How should humans remain in control of important decisions? - How should production AI systems scale safely? - How should memory be designed for enterprise AI? - How should AI systems balance operational performance with governance, security, and reliability? - How should AI systems create measurable business value? If those questions excite you, you'll probably enjoy working with us. WHAT MAKES SOMEONE SUCCESSFUL HERE We're looking for engineers who: - Think in systems rather than individual features. - Care deeply about production quality. - Enjoy solving ambiguous technical problems. - Communicate complex ideas clearly. - Balance speed with sound engineering judgment. - Build practical solutions rather than chasing hype. - Continuously learn, experiment, and improve. We're significantly more interested in systems you've built than technologies you've used. Please provide specific examples that demonstrate your role, engineering decisions, and measurable outcomes. We recognize that many engineers use AI as part of their workflow. You're welcome to do the same. However, your application should accurately reflect your own experience, judgment, and technical thinking. We respect the confidentiality of your current and former employers, clients, and partners. Please do not include proprietary or confidential information in your application. Describe your work at a level that demonstrates your engineering approach without disclosing protected information. PROFESSIONAL STANDARDS We value integrity, sound engineering judgment, and respect for intellectual property. Please do not include confidential, proprietary, export-controlled, or other non-public information belonging to your current or former employers, clients, or partners in your application or work samples. We're interested in your engineering approach, architectural thinking, and problem-solving methodology – not protected information belonging to others. If you share code, architecture diagrams, technical documentation, or project examples, please ensure you have the legal right to do so and identify any material open-source or third-party technologies where appropriate. By submitting application materials, you represent that you have the legal right to share them and that doing so does not violate any confidentiality, intellectual property, employment, consulting, or other contractual obligations. Any engagement, if offered, will be subject to a separate written agreement covering confidentiality, intellectual property ownership, compensation, and other applicable terms. Submission of an application or participation in the evaluation process does not create any employment, independent contractor, partnership, joint venture, agency, fiduciary, or other business relationship with 26ers AI, nor does it obligate either party to enter into any future engagement. 26ers AI reserves the right to evaluate applications, discontinue discussions, modify the hiring process, or decline to pursue any engagement at its discretion. Nothing in this posting should be construed as an offer of employment or an offer to contract.

Title: Backend Developer — AI Data Pipeline, Vector DB & Real-Time Push API Post: We are building an automated backend system that continuously crawls public web sources, processes and indexes content using AI, and delivers updates via webhooks. Looking for someone who has built this type of system before and can move fast. NDA required before project details are shared. What you’ll build: • Web crawler network —. • AI processing pipeline — cleans, deduplicates, chunks, and embeds ingested content into a vector database using an LLM embedding model. Quality scoring and incremental updates required. • Push API — monitors for significant content changes and delivers updates via webhook endpoints automatically. Includes configurable push schedules per subscriber, REST query endpoint, API key authentication, and token usage tracking per key. Tech stack (flexible — use what you know best): • Python (FastAPI) or Node.js • Any vector DB — Pinecone, ChromaDB, Supabase • Any LLM API — Anthropic or OpenAI • Any scheduler — n8n, APScheduler, cron • Redis for queue management • Railway, Render, or AWS for deployment Requirements: • NDA signed before kickoff — non-negotiable • Must have built RAG pipelines or vector DB systems in production — not tutorials • Must have experience with web crawlers and scheduled job pipelines • Must have experience with webhook delivery systems • GitHub or portfolio showing relevant deployed work required • 95%+ Job Success Score preferred • Individual contractors only — no agencies To apply include: • Example of a similar system you’ve built — web crawler, RAG pipeline, or push notification API • Your preferred stack for this type of build • Brief technical approach in 3–5 sentences • Hourly rate and availability to start Budget: $50–$80/hr Timeline: 3 weeks — focused sprint with daily check-ins

Overview We run an AI voice assistant for self-storage operators. We have an internal, AI-assisted workflow for triaging call feedback — investigating what happened on a call, diagnosing the root cause in our codebase, and drafting fixes. We’re looking for someone technical to run that AI-assisted workflow day to day and help us make it better. You’ll be driving AI coding agents, reading real code to understand behavior, and improving the process and tooling itself. What you’ll do Use our AI agent tooling to work through a queue of customer feedback on AI voice calls. Read our TypeScript/Node codebase (voice-agent prompt assembly, workflow/“SOP” engine, tool implementations) to diagnose why the agent behaved a certain way — not just guess. Draft fixes: workflow-instruction edits, knowledge-base entries, or code changes via pull request with a clear verification plan. Improve the triage process itself — refine the AI agent prompts/skills, conventions, and the internal MCP tooling that powers it. Write clear, customer-facing summaries of what changed for our team to review and approve. You’re a great fit if you Read and reason about code confidently — ideally TypeScript/Node; React a plus. Have hands-on experience driving AI coding agents (Claude Code, Cursor, or similar) and understand how LLM prompts/tools/agents fit together. Think in cause-and-effect: “the agent did X because line Y / instruction Z.” Write precisely and concisely for both technical and non-technical audiences. Are process-minded — you spot the repetitive thing and turn it into a better workflow. Bonus: prompt engineering, LLM tool/agent development, or voice/conversational AI experience. How we work We’ll start with a paid trial on a small batch, then scale steady ongoing volume. To apply: Tell us about a time you used an AI coding agent to diagnose or fix something non-trivial in a codebase you didn’t write — what you did, and how you verified it worked. A link to relevant work is a plus.

US-BASED CONTRACT FULL-STACK / AI ENGINEER FOR HEALTHCARE PRODUCT Remote, US-Based Contractor | East Coast Time Required | Home Health AI Product About Scribble Scribble is the top-rated AI platform for home health purpose-built to give clinicians their time back. Home health agencies run on documentation: visit notes, care plans, prior authorizations, compliance paperwork - we help automate these processes. Project Overview We are looking for one or more experienced US-based contractors to help us move faster across several important areas of our product. We need hands-on builders who can scope clearly defined product needs, ask the right questions, and independently deliver high-quality work. This is not a narrow ticket-taking role. We are looking for people who can own meaningful pieces of the product, communicate clearly, work asynchronously, and make steady progress without heavy day-to-day management. Depending on your strengths, the work may focus on mobile, backend, AI workflows, and EMR robotic process automation. You will ideally own end to end development including testing. What We Need Help With • Build and improve visit types and clinical documentation workflows for home health use cases • Improve accuracy of AI-generated outputs, including prompt design, evaluation, testing, and workflow refinements • Integrate completed visits and documentation into EMR systems using APIs where available and robotic process automation where needed • Work across React Native mobile app features, Node.js backend services, OpenAI/LLM workflows, and automated testing • Debug production issues, improve reliability, and help us ship quickly without sacrificing quality • Troubleshoot and fix bugs quickly, and make product improvements based on customer feedback • Translate product requirements into practical technical plans and independently execute against them • Document decisions, provide frequent updates, and proactively flag risks, blockers, and trade-offs • Potentially collaborate with team members across different parts of the roadmap Skills We Are Looking For Required • Advanced use of Claude Code • US-based contractor with East Coast time zone availability required • Strong experience with React Native mobile development • Backend experience with Node.js, APIs, databases, authentication, and production debugging • Hands-on experience with OpenAI or other LLM APIs, prompt engineering, structured outputs, and AI workflow testing • Strong testing mindset, including unit tests, integration tests, regression testing, and quality checks for AI outputs • Ability to work independently from a product goal, break it into technical tasks, and deliver without constant direction • Excellent written and verbal communication; concise updates, clear questions, and proactive status reporting are essential • Speed and quality are both must-haves: we need someone who can move quickly while still shipping reliable, well-tested work • Comfortable working with sensitive healthcare data and following HIPAA-aware, security-conscious development practices Strongly Preferred • Meaningful healthcare experience is strongly preferred, especially in home health, clinical documentation, EMR/EHR workflows, HIPAA-aware development, or regulated healthcare environments Contract Details • Contract role for a US-based independent contractor • US-based candidates only; East Coast time zone availability is required • Part-time or project-based to start, with potential for ongoing work • Minimum availability of 20 hours per week is required • We may hire multiple contractors based on specialty, fit, and availability • Clear deliverables, frequent communication, and fast iteration cycles • Hourly rate or fixed-price milestones can be discussed based on scope and experience • Selected contractors will need to sign appropriate IP assignment, NDA, and Business Associate Agreement documents before accessing sensitive product or healthcare data • We may request and check references before starting a larger engagement

NobleProg is seeking an experienced AI Trainer to deliver a live, instructor-led remote training focused on helping technical professionals integrate Agentic AI and RAG systems into their existing workflows. This opportunity is designed for participants with strong technical backgrounds (Data Engineering and Workflow Automation) but limited formal AI experience, with the goal of applying AI to real-world systems rather than learning theory. Engagement Details Location: Remote Duration: 2 days Audience: Data Engineers and Workflow Developers Participants: 4+ Daily Rate $1,100 per day Course Scope This training focuses on practical, hands-on development of AI-powered systems using Retrieval-Augmented Generation (RAG) and agent-based architectures. The course will follow a Core & Split approach, starting with shared foundational concepts, moving into role-specific deep dives, and concluding with an integrated session demonstrating how AI systems are built and applied across workflows and data pipelines. NobleProg SOP - https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Key Topics Include - Practical introduction to LLM applications and AI system architecture - Retrieval-Augmented Generation (RAG) design and implementation - Data preparation, embeddings, and vector database concepts - Agentic AI fundamentals (tools, decision-making, multi-step workflows) - Orchestration frameworks such as LangChain, LangGraph, or similar - Role-based applications: RAG pipelines for data engineers and AI-driven workflows for workflow developers - End-to-end system integration (RAG + agents + automation) Trainer Responsibilities - Deliver engaging, instructor-led remote training with strong hands-on focus - Translate AI concepts into practical applications for non-AI technical professionals - Structure delivery using a Core & Split model to address different roles - Provide real-world exercises aligned with data pipelines and workflow automation - Facilitate an integrated session demonstrating how different components work together - Prepare training materials (trainer retains ownership of content) Required Qualifications - Hands-on experience building LLM-based applications, including RAG systems and agent-based workflows - Strong proficiency in Python and experience with APIs, data pipelines, or automation systems - Experience with frameworks such as LangChain, LangGraph, or similar - Proven experience delivering technical training to engineering audiences - Ability to simplify AI concepts and connect them to real-world use cases Nice to Have - Background in data engineering, workflow automation, or solutions architecture - Familiarity with MCP or emerging agent orchestration frameworks - Experience designing modular or role-based training programs preferred - Experience building production-grade AI applications preferred https://docs.google.com/document/d/184VlJipyixkLNJ_HnP3aPt4YToedTUAlji_LxkuLhRU/edit?usp=sharing Please review and approve this tentative outline. We will be meeting with the client to determine whether they prefer a 1-day or 2-day delivery format. The agenda may require some adjustments based on the client's specific objectives, technical background, and areas of interest, which can be finalized during the trainer-client consultation call. Could you please review the proposed outline and let us know if you see any red flags, gaps, concerns, or topics that may require immediate attention? We would also appreciate any recommendations regarding scope, level of technical depth, hands-on exercises, or prerequisite knowledge that should be addressed before presenting this to the client. Thank you for your feedback. How to Apply Please include - A brief overview of your experience with Agentic AI and RAG systems - Your experience delivering technical or AI-focused training - Examples of AI systems or applications you have built - Your approach to teaching participants without formal AI background - Availability for remote delivery