We are a fast-growing telecom / AI-First CPaaS serving sms and voice API's. We are building the first AI-first communications platform (SMS, Voice, RCS, AI agents) designed for speed, simplicity, and real-world business outcomes. We are not looking for a “task completer.” We are looking for a true senior engineer who: thinks in systems moves fast makes decisions independently writes clean, scalable code uses AI tools (Claude, etc.) as a force multiplier ⚠️ Read This First *DO NOT APPLY IF YOU ARE PRETENDING TO BE IN A DIFFERENT COUNTRY. PROOF OF RESIDENCY IS REQUIRED. Most applicants will not be a fit. If you need: detailed tickets hand-holding constant direction This is NOT the role for you. If you are the type of engineer who: sees a problem and solves it end-to-end improves architecture without being asked ships quickly without sacrificing quality You will thrive here. What You’ll Do Build and ship full-stack features across our platform (messaging, voice, AI workflows) Make architectural decisions (not just implement) Improve system performance, reliability, and scalability Work directly with founders (no PM layers) Move from idea → production very quickly What We Expect (Non-Negotiable) 5+ years real full-stack experience (not just titles) Strong backend experience (Node.js / APIs / infra) Strong frontend experience (React or similar) Experience building production systems at scale Ability to work autonomously with minimal direction High ownership mentality Bonus (but highly valuable) Experience with telecom / CPaaS / messaging Experience with AI integrations (LLMs, agents, workflows) Experience optimizing performance at scale Startup experience (especially early-stage or fast growth) How We Work Small, high-output team Very fast iteration cycles No unnecessary meetings High trust, high expectations We use AI tools heavily (Claude, etc.) — you should too What We Care About Most Not your resume. We care about: How you think How you build How fast you execute The quality of your code To Apply Please include: Links to projects you’ve built (real production work) A short explanation of: a system you designed end-to-end a difficult technical decision you made independently Your GitHub Optional (but strong signal): Share how you use AI (Claude, etc.) in your workflow Compensation Competitive (based on experience) Long-term opportunity with a fast-growing, profitable company If you are truly senior, this will feel obvious. If not, this role will be very uncomfortable. **THIS IS A FT, HOURLY ROLE. PROVIDE YOUR REQUESTED HOURLY RATE IN PROPOSAL**

Add Tests, Security Audit for Credit Cards, Possible Refactor: Vibe-coded Social Graphify + Lovable: Do not use ai to write your proposal, or for any of your communication. I do not need AI or an AI detection tool to know. It's obscene. I can't trust Claude to make tests. It always slyly reverts when making a productive change to changing the tests to mock data. So I need a human to implement them. Full-stack web dev js, python, llm's. Additional background or interest in: Graph Theory, Graph Neural Nets, Graphical Probabilistic Models, Bayesian Neural Nets, Category Theory, Pre-Deep Learning Natural Language Processing (Cfg's, etc.), Semantic Web Tech (rdf/owl/xbrl), Library Sciences, Pre-LLM Machine Learning (+Stat/Econometrics/etc.), Federated Learning and Crypto is appreciated. Do not use ai to write your proposal, or for any of your communication. I do not need AI or an AI detection tool to know. It's obscene.

OVERVIEW I'm building Inkora, a subscription web app: an AI creative-writing companion for fiction (romance, fan fiction, poetry, general fiction). The React front-end is COMPLETE and will be provided, with clearly marked integration points. I need the backend built, connected, and the full app deployed. This is an 18+ platform with content safeguards. STACK ALREADY IN PLACE - React front-end (provided; single-file app ready to be scaffolded into a project) - GitHub (repo will live on my account) - Vercel (hosting, my account) - Stripe account created - Anthropic (Claude) API key — must live server-side only, never in client code - Domain: inkora.com SCOPE OF WORK 1. Secure AI endpoint — a serverless endpoint (e.g. POST /api/write) that attaches the provided system prompt, calls the Claude API with the server-side key, and returns the text to the front-end. Suggested writing model: claude-sonnet-4-6. 2. Content moderation — screen user input BEFORE the model call and model output AFTER it. Block sexually explicit content and gratuitous graphic gore. Use a inexpensive-model screen (e.g. Haiku) or a third-party moderation API. Log violations; support a warn-then-suspend flow. 3. Auth — your recommendation (Supabase or Firebase preferred). Email + password baseline; Google sign-in if low effort. 4. Database — persist each user's stories (title, genre, full message history, timestamps), characters, and notes. The front-end already models these data shapes. The story's full saved history must be sent with each AI request (this powers in-story continuity). 5. Subscriptions (Stripe) — $10/month, ~$27/quarter, ~$96/year. Free tier: 10 messages per day (make this a config value), resetting every 24h. Gate writing access by subscription status / remaining free allowance. Handle the subscription lifecycle via webhooks. 6. Age-gate persistence — the front-end collects date of birth + an 18+ affirmation; store the affirmation timestamp with the account. Structure it so a third-party age-verification provider can be swapped in later without reworking the flow. I'm open to building on a proven starter/template (e.g. the Vercel AI chatbot template) if it speeds things up — adapting solid existing code is welcome. DELIVERABLES - Deployed, working app (provided front-end + your backend) on my Vercel, repo on my GitHub - All secrets configured as environment variables (never in client code) - Short handoff notes: key rotation, where moderation logs live, how to change config values WHAT I PROVIDE - Complete front-end code, a written backend brief, and the system prompt (shared privately with the selected freelancer) - All accounts (GitHub, Vercel, Stripe, Anthropic) already created and ready TO APPLY, PLEASE ANSWER THESE IN YOUR PROPOSAL: 1. Have you wired Stripe subscriptions with webhooks before? Link or example. 2. How will you keep the API key off the client? 3. How would you implement input + output moderation cost-effectively? 4. Have you deployed serverless APIs on Vercel? 5. Your fixed quote and timeline for this scope. Proposals that don't answer these questions will not be considered.

Looking for general help building out a platform for an AI saas.

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 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.

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.

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.