I'm looking specifically for a US-licensed attorney whose core practice is drafting and negotiating commercial leases — office, industrial, or retail. Not lease litigation, not general real-estate transactions, not broad contract work. US commercial-leasing practice specifically: if that isn't the center of what you do, this isn't a fit, and I'd rather say so up front than waste your time. (I can only work with attorneys licensed and practicing in the United States — several of the questions turn on US market-standard leasing practice, so non-US experience won't transfer here.) I have a narrow, fixed-scope task. It is NOT a lease review. I've built a tool that classifies commercial-lease provisions, and I'm calibrating it against experienced CRE attorneys' independent judgment. I'd like your read on a set of clause excerpts — four short clause-areas, ten quick questions total. For most, one word is the whole answer: you pick a bucket (Risk / Review Needed / Improvement / Standard). Two of the ten have a one-line follow-up. About 25 minutes. Several of these turn on what's market-standard in US commercial leasing — not just whether a clause reads as risky — so I need someone with real transactional volume, not adjacent experience. To show you the kind of thing I mean, here is one representative example — NOT from the actual set, just so you can see the format: Clause: "Tenant shall not assign this Lease or sublet all or any portion of the Premises without the prior written consent of Landlord, which consent Landlord may grant or withhold in its sole and absolute discretion. Any assignment or sublease without such consent shall be void at Landlord's option." Question: From a tenant's perspective, how would you bucket this assignment-and-subletting provision? [Risk / Review Needed / Improvement / Standard] That's the whole rhythm — a real clause, a one-word bucket, occasionally a one-line note. Ten of those across four clause-areas. The one firm rule: I deliberately will NOT tell you how my tool classified anything, and I'd ask you not to look the clauses up. Your unprimed, cold read is the entire value here — if you try to reverse-engineer what I'm hoping to see, the exercise is worthless to me. So please just answer from the text as given, in your own judgment. This is a paid engagement: a fixed price of $150 for ~25 minutes, fully async — I send a Word doc; you send your answers back in writing — no call needed. One thing to flag so it's not a surprise: after I read your answers, I may have one or two short clarifying questions — a sentence each, nothing involved. That's included in the flat fee, not a separate ask. And if anything ever looked like it needed more than a quick reply, I'd say so and we'd agree on it first — I won't quietly expand the scope on you.

We're a fast-moving studio building several SaaS products. The first — closest to launch — is a tool for churches and pastors. A lot of our app is built fast with AI-assisted workflows, which makes it functional but generic-looking. Your job is to give it taste: establish a clean, reusable design system and redesign our core screens so the product feels premium, trustworthy, and genuinely beautiful — then hand polished, dev-ready designs to our developers to build. This starts as a focused contract project — a design system + a redesign of our launch product's key screens — with strong potential to continue ongoing as we add more products. What you'll do Audit our current screens and redesign them into something clean, modern, and premium Build a reusable design system (components, color, typography, spacing, patterns) that our developers build within — so quality stays high without you touching every screen Design user flows and mockups for new features — improve usability, not just looks Hand off dev-ready designs in Figma (organized, spec'd, with all states) that our developers + AI build from accurately Review the built screens against your designs and polish the flows that matter most Bring brand sensibility — for our faith product that means calm, warm, reverent, and trustworthy, not flashy Our setup Our developers build in Next.js + React + Tailwind CSS (often with shadcn/ui). Designs that map cleanly to that world — and a smooth Figma → code handoff — make everyone faster. You don't need to write code, but understanding how design becomes front-end is a real plus. You're a great fit if you Have a portfolio of real SaaS / web-app product design (not just landing pages or logos) Have built or worked within a design system Have strong taste and restraint — you make things beautiful without over-designing Think about user flows and usability, not only visuals Communicate clearly and work independently Can overlap a few hours with US time zones Nice to have Can do light front-end yourself (a "design engineer" who designs and builds in React/Tailwind) Experience designing for faith, community, or trust-sensitive products Comfortable designing within / extending shadcn/ui + Tailwind To apply — answer these (applications without answers are skipped) Share 1–2 SaaS or web-app products you designed. What was the before/after, and what problem were you solving (not just "made it prettier")? Have you built or worked within a design system? Briefly, how? How do you hand designs to developers so they build them accurately? Your part-time weekly availability and US time-zone overlap. Shortlist step (paid) A short paid trial: we'll give you one real screen from our app and ask you to redesign it plus a few sentences on your reasoning. We hire from the trial, not the résumé.

Are you a relationship-driven sales professional who loves helping small, independent businesses modernize and thrive? We are an official Google Cloud Partner specializing in Google Workspace sales and service. We are looking for an independent, US-based Sales Specialist to help us identify, engage, and close deals with small businesses. Note on Compensation: We are intentionally leaving the exact pay structure open. Whether you prefer a baseline hourly rate + commission or a highly leveraged commission-heavy structure, we want to hear your pitch. We will use initial engagement and feedback to schedule 30-minute exploratory calls with top-tier candidates. What You Will Do: Hunt & Close: Proactively find and close Google Workspace deals within the SMB market. (We are not looking for someone to grow our service/implementation practice; your focus is purely on sales and market expansion). Consultative Selling: Conduct discovery calls and video consultations via Google Meet with small business owners to understand their needs and position Google Workspace as the ideal solution. Relationship Management: Act as the professional, American-based face of our sales front. Our Target Clientele Includes: Independent medical practices and law firms Home service professionals (plumbing, electricians, contractors) Local automotive repair shops Real estate agents and service industry professionals Boutique retail stores, restaurants, and local "mom-and-pop" shops Requirements: Location: Must be located in the United States. Communication: Exceptional phone and video presence (comfortable running Google Meet demos). Experience: Proven track record in B2B sales or marketing, specifically working with small business owners. Tech-Savviness: You don’t need to be an engineer, but you must be comfortable using modern tools. Pluses (Not Required, but a Major Advantage): Prior experience in IT, SaaS, or tech sales. User-level familiarity with cloud collaboration platforms (Google Workspace, Microsoft Office 365) and modern AI tools. What We Offer (The Perks): Paid Onboarding Training: As a Google Cloud Partner, we have mandatory product modules and quizzes. We will compensate you for 100% of the time you spend going through these self-paced training modules. Paid Professional Certification: We will fully fund your official Google certifications and class materials. (Studying and exam-taking will be done on your own time, but all resources are paid for by us). Flexibility: Work on your own schedule, from anywhere in the US. How to Apply: Please submit a brief proposal detailing your experience selling to small businesses. To filter out automated spam, please start your proposal with the phrase "Google SMB Expert" so I know you read this entire description. Tell us a bit about your preferred compensation structure (Hourly vs. Commission vs. Hybrid) and your general availability for a 30-minute introductory call.

What this is: I'm the founder of a working AI SaaS platform (FastAPI/uvicorn backend, React/Vite frontend, deployed on AWS). The product pipeline is built and live! I need an experienced engineer to build the authentication, payments, and usage-entitlement layer that gates the product behind login and paid usage. You'll work against a runnable stub repository (real routes + data shapes, proprietary internals removed) - so you can build and test the full layer without ever touching the core IP, and afterwards - I will merge your work into the production app! Scope: • Auth (Clerk): signup/login, session lifecycle, backend session/JWT verification, inject a validated `user_id` into protected endpoints. • Payments (Stripe): Checkout for purchasing usage credits, plus a webhook that credits the user's ledger on successful payment. The webhook MUST be signature-verified, idempotent (Stripe retries-credit exactly once), and reconcilable (handle webhook-failed-but-payment-succeeded). • Entitlement ledger (the core): a credit balance per user with atomic check-and-decrement (two concurrent requests must never double-spend) on PostgreSQL. A `check_entitlement(user_id, ...)`gate that expensive endpoints call before doing paid work. • Server-side route gating: enforce entitlement on every paid endpoint (not just hidden in the UI); clean 401/402/403 responses. • Frontend integration (React): login/signup UI, buy-credits/checkout flow, balance display,graceful "out of credits" states, wired to the above. • Tests for the money-critical paths (webhook idempotency, atomic decrement, gating-bypass attempts). Stack you'll work with: Python 3.11, FastAPI, Pydantic, uvicorn (Linux). React + Vite, Tailwind. PostgreSQL. Clerk. Stripe. Who I'm looking for: Someone who has shipped this exact kind of system to production before. In your proposal, briefly tell me: 1. How you ensure Stripe webhook idempotency and handle reconciliation when a webhook fails but payment succeeded. 2. How you'd implement the atomic credit check-and-decrement so concurrent requests couldn't double-spend. 3. A relevant production system you've built (Stripe + auth + usage metering ideally). *I'd much rather a short, specific proposal - than a long generic one!* Working with me: I'm an engaged founder who genuinely enjoys building, and I want this to be a good experience for both of us! I'm responsive, communicate clearly, and I respect your expertise - because you're the specialist here,and I'm hiring you because you know this much better than I do! I'm not a micromanager and I'm not nitpicky - I just want the end results to be solid! I review in milestones, asking questions because I like understanding the work (not because I'm second-guessing you), and I make decisions quickly so you're never stuck waiting on me! The scope is already well-defined (detailed spec + a runnable stub repo on engagement), so you're not pricing around a vague, shifting target - it's a clean, bounded build. I care about getting the money-handling pieces right, and I'm betting you do too - if that resonates, I think we'll work well together! IP / engagement: A mutual NDA + IP-assignment (work-for-hire) before repo access. You build against stubs; you won't receive the proprietary pipeline. Work is milestone-based, fixed-bid preferred. To apply: Answer the three questions above + share 1-2 relevant examples, and ballpark your timeline and milestone structure (suggested: auth → payments+webhook → ledger+gating → frontend → tests/docs)!

We are looking for a hands-on Forward Deployed AI Engineer to help build practical AI systems This is not a pure backend role and not a strategy-only consulting role. You will work close to end users, understand how their workflows actually operate, and then build AI-enabled tools that solve specific business problems. The ideal person is a strong software engineer who is comfortable with ambiguity, can communicate clearly with non-technical stakeholders, and can take an AI prototype from idea to something reliable and usable. What you will do - Learn the business workflows, systems, data, and constraints. - Build AI applications using Claude or similar large language models. - Use the right mix of prompting, retrieval, tool use, agents, and workflow automation. - Own delivery from scoping through prototype, testing, hardening, and handoff. - Create evaluations to determine whether the system is accurate, reliable, and safe enough to use. - Translate between domain experts and technical implementation. - Work carefully with sensitive or regulated data. - Document what you build so it can be maintained and reused. What we are looking for - Strong Python engineering skills. - Hands-on experience building with LLMs, preferably Claude or the Anthropic API. - Experience with RAG, structured prompting, tool use, evaluation, or agentic workflows. - Ability to operate independently in a messy, ambiguous environment. - Strong communication skills with both technical and non-technical stakeholders. - Track record of shipping working software, not just demos. - Comfort working with real-world data, integrations, and imperfect requirements. Helpful but not required - Prior forward deployed engineering, solutions engineering, or technical consulting experience. - Experience building AI tools for enterprise customers. - Experience in regulated or sensitive-data environments. - Familiarity with validation, auditability, traceability, or compliance-oriented workflows.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.

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.

We are hiring an AI Engineer with strong hands-on experience building and shipping real AI products. Requirement: If you don't have a GitHub profile to share, this role is not a fit. What we’re looking for: • Strong experience in AI/ML engineering • Ability to build, test, and deploy production-ready AI systems • Practical experience working on real-world AI projects To apply: Please share your portfolio, past AI projects, and relevant work samples. Applicants without portfolio will be ignored.

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.

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