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.

Project Title: Build Fast Web-Based AI Anime Companion MVP (RAG + Merch Gen) – 15–30 Days, $25K Budget: $18,000–$25,000 fixed Timeline: 15–30 days (3 weeks preferred) Must-Have Skills: FlowiseAI (or LangChain/LlamaIndex), RAG pipelines, OpenAI/Claude/Grok, anime image generation (Leonardo.ai/Ideogram/PixAI), Vercel or Railway deployment Project Goal Build a mobile-friendly web AI Companion for Big A Anime that converts passive FAST viewers into active fans. The key objective is to connect our Pluto TV channel experience directly to the AI companion, allowing viewers to scan QR codes during live programming and instantly access interactive content, recommendations, and merch tied to what’s currently airing—no downloads required. Core MVP Features Web Chat Interface Clean anime-branded chat UI with voice input Mobile-first, responsive Custom domain (e.g., companion.biganime.tv) RAG Knowledge Base Ingest episodes, schedules, transcripts, and metadata Provide accurate recaps, lore, and “what’s on now/next” tied to Pluto TV programming Session memory + light user profiles AI Merch Generator Anime-style image generation (“me as [character]”) Leonardo.ai or similar integration Export + links to Printful/Printify FAST / TV Integration Tools Dynamic QR codes for on-screen use Deep linking between Pluto TV programming and companion experience Voice-friendly prompts (“Ask Big A Companion…”) Admin & Analytics Simple CMS for content uploads Dashboard: usage, queries, merch clicks Technical Requirements Global hosting (CDN) FlowiseAI preferred Full source + documentation 30 days post-launch support Out of Scope Native apps, payments, deep integrations, multi-language Deliverables Live URL, admin access, training, source code, 30-day support Application Fixed bid + 3-week plan 2–3 relevant project links Willingness for small paid test ($500–$800)

Senior AI/ML Engineer / Claude architect — Legal Tech FirmProfit AI is the operational backbone of the modern law firm. We automate law firm operations end to end, and we're looking for a top-tier AI/ML engineer to help us build the next major platform in legal tech. We need a true expert. Someone deeply proficient with Claude and modern LLM architecture who has shipped real products at a high level. You're fluent across the full stack with Node.js, React, Postgres, MongoDB etc... and you have hands-on experience building with LangChain, LangGraph, MCP, and AWS Bedrock. We're not looking for someone who's read about LLMs. We're looking for someone who has shipped agents, orchestration layers, and production AI systems that real users depend on every day. Our current team is 8 engineers, we have firms signed and live, and we're moving fast. This is a chance to come in early, and have your work in the hands of customers within weeks. Contract to start, with a long-term path for the right person. Reply with the most impressive AI product you've shipped.

I am looking for an experienced ASR engineer to build a production-ready speech-to-text system for a low-resource language. I already have approximately 3,000 prepared audio segments, totaling about 10 hours of audio, with clean and consistent transcripts ready for immediate use. Data preparation and segmentation are already handled. The initial 10 hours of audio will serve as the first milestone. After that, the engineer will be expected to continue training and improving the model with additional data until the system reaches a target WER of 10% or below. Your responsibility will focus on: Fine-tuning a Whisper-based model for high transcription accuracy Optimizing word error rate (WER) over time Providing inline/embedded start timestamps per phrase Building an efficient inference pipeline for both real-time and batch transcription Structuring evaluation and improvement workflows Preparing the system for deployment and integration into a web platform Providing clear documentation and guidance so I can independently continue training and improving the model over time without ongoing engineer involvement The goal is to reach strong accuracy at launch, with a clear process for continued improvement as more data becomes available. Please describe your experience with Whisper fine-tuning or similar ASR model training in your proposal.

We're looking for an AI & Data Analysis expert to lead the integration of intelligent tools within the business platform. You'll connect Google Ads, marketing data files, and operational data sources to build AI agents via Claude that support business decision-making across our pet retail operations. Key Responsibilities Design and configure Claude-powered agents using tool use, structured prompts, and automated workflows for data analysis Integrate the Google Ads API to extract campaign metrics and feed decision-making dashboards Ingest, clean, and structure CSV, Excel, and other marketing data formats for agent processing Generate automated narrative reports and actionable visualizations for the executive and marketing teams Maintain and iterate on data pipelines connecting advertising, sales, and inventory data Required Technical Skills Claude API / Anthropic MCP (Model Context Protocol) Prompt engineering and LLM tool use / function calling Google Ads API Python or JavaScript (for pipelines and integrations) SQL / PostgreSQL / Supabase Pandas / NumPy or equivalent data libraries REST API consumption and integration Advanced Excel / Google Sheets Nice to have: Google Analytics, BigQuery, Looker, Power BI Ideal Profile Proven experience building data pipelines or LLM-powered tools in a production environment Hands-on familiarity with the Anthropic API and agent/tool-use patterns Ability to translate raw data into clear, actionable business recommendations Self-directed — can propose and build solutions without exhaustive specs Initial Projects Campaign ROI Agent — connects Google Ads + business sales data to generate automatic performance alerts and recommendations Marketing File Pipeline — ingests CSV/XLSX marketing files and produces AI-generated summaries and insights Executive Dashboard — decision-support interface with Claude-generated action recommendations based on live data

We are a small residential real estate investment company seeking an AI Solutions Architect to enhance our acquisition platform. The role involves designing and implementing AI solutions to improve data analysis and decision-making processes. The ideal candidate will have experience in AI architecture and a strong understanding of real estate data analysis.

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 seeking a Machine Learning Engineer with a strong background in Computer Vision and ML fundamentals. The ideal candidate will have experience in healthcare and automotive domains. This role requires someone based in the US, with the ability to work for 6+ months. The candidate should be able to integrate into our team seamlessly and contribute to ongoing projects effectively. [IMPORTANT] In order to verify your language preference. please attach your 1 or 2 mins intro video.

We are seeking an experienced AI workflow / operations consultant to help us build repeatable “business of the future” blueprints for niche service industries. We are not building a new AI product. We want help identifying the best existing tools, workflows, and automation opportunities to modernize service businesses in a practical, scalable way. We want someone who can help us: map current workflows identify the best opportunities for AI and automation recommend the right tool stack define what should stay human vs. what should be automated create standardized implementation blueprints we can deploy across multiple clients We are looking for someone with real operational depth, not just generic AI enthusiasm. Bonus if you have worked in: healthcare legal compliance-sensitive service businesses CRM / automation / intake / communication systems Please send examples of similar work and explain how you would approach building a scalable modernization blueprint for these kinds of businesses.

AI Automation Engineer – Personal Injury Law Firm (Phase 1) Overview We are a plaintiff personal injury law firm seeking an experienced AI automation engineer to build a practical AI-powered operations system that reduces administrative workload and improves case management. Firm Profile: - 1 Attorney - 1 Legal Assistant - Approximately 100 Active Cases - Approximately 25 Cases in Litigation We are not looking for chatbots, prompt engineering, or marketing automation. We are seeking a builder who can deploy production-grade workflow automation integrated with our existing systems. --- Existing Technology Stack - CASEpeer - Supio - Gmail (Google Workspace) - Google Calendar - Google Drive - Claude - ChatGPT - CoCounsel - PLAUD --- Phase 1 Objective Build a Daily Case Command Center and Follow-Up System. The goal is to ensure that every morning the attorney knows exactly which files require attention and why. --- Deliverable #1: Daily Case Command Brief Generate a daily briefing containing: Litigation Matters - Upcoming depositions - Discovery deadlines - Hearings - Expert deadlines - Outstanding litigation tasks Pre-Litigation Matters - Treatment complete but no demand - Missing medical records - Records requests outstanding more than 21 days - Demands pending more than 30 days - Settlement checks outstanding Communications - Unanswered client emails - Unanswered adjuster emails - Unanswered defense counsel emails - Communications requiring attorney attention Case Velocity - Stale files - Cases with no recent activity - Recommended next actions --- Deliverable #2: Gmail Intelligence The system should: - Monitor designated Gmail inboxes and labels - Identify case-related emails - Extract action items - Detect deadlines - Identify follow-up opportunities - Draft suggested responses --- Deliverable #3: Follow-Up Automation Generate draft communications for approval: - Medical records requests - Provider follow-ups - Adjuster follow-ups - Client status updates - Scheduling communications No communication should be sent automatically. Human approval is required before sending. --- Deliverable #4: Calendar & Reminder Automation Create and manage: - Follow-up reminders - Litigation reminders - Discovery reminders - Records request reminders - Suggested calendar events --- Deliverable #5: CASEpeer Integration Where supported by available integrations/APIs: - Create tasks - Create notes - Associate communications with matters - Maintain activity history --- Technical Requirements Required: - n8n - Python - OpenAI API - Anthropic API - Gmail API - Google Calendar API - Google Drive API - REST API integrations Strongly Preferred: - LangGraph - LangSmith - Google Workspace administration - Salesforce integrations - CASEpeer, Clio, or Filevine experience - Legal technology experience - HIPAA or regulated-data experience --- Security Requirements - Human approval before external communication - Activity logging - Error handling - Retry mechanisms - Secure credential management - Documentation of workflows --- What Success Looks Like The system should: - Reduce administrative workload - Improve follow-up consistency - Reduce stale files - Improve litigation oversight - Improve case visibility - Provide a daily prioritized action plan --- Application Requirements Please include: 1. Similar workflow automation projects you have completed. 2. Experience with n8n and AI workflow automation. 3. Experience integrating Gmail and Google Workspace. 4. Experience integrating CRM or case-management systems. 5. Proposed architecture for this project. 6. Estimated timeline. 7. Estimated fixed-fee budget. Begin your application with: CASECOMMAND Applications without this keyword will not be considered. --- Budget Expected Phase 1 Budget: $8,000–12,000 Preference will be given to candidates who can demonstrate production deployments rather than proof-of-concept or prompt-engineering projects.