We are looking for a strong software engineer who can build practical automation systems using AI, APIs, and modern development tools. This role is for someone who can take messy business workflows, understand the goal, and build working systems that save time, reduce manual work, and improve execution. You should be comfortable building automations, integrating tools, working with APIs, writing clean code, and using AI tools like OpenAI, Claude, or similar models to create useful business applications. What You’ll Work On You will help build and improve systems such as: AI-powered research and data extraction workflows CRM and sales process automations Email, spreadsheet, and database automations Internal tools and dashboards API integrations between business software Web scraping and data enrichment workflows when appropriate AI agents or assistants that help with repetitive business tasks Automation around deal screening, reporting, lead research, and document creation Ideal Candidate We are looking for someone who is practical, fast, and can figure things out without needing step-by-step instructions. You should have experience with: Python and/or JavaScript APIs and webhooks OpenAI, Claude, or other LLM APIs Automation tools like Zapier, Make, n8n, Airtable, Google Sheets, HubSpot, Salesforce, or similar Databases such as PostgreSQL, Supabase, Firebase, or similar Basic front-end or internal tool development Web scraping, data cleaning, and structured data workflows GitHub and clean documentation What Matters Most We do not need someone who only talks about AI. We need someone who can actually build. The right person should be able to: Understand a business process quickly Recommend the simplest technical solution Build fast prototypes Turn prototypes into reliable workflows Communicate clearly Document what was built Improve systems over time Nice to Have Experience with any of the following is a plus: Private equity, M&A, finance, or investment workflows Deal sourcing or lead generation systems CRM automation Data enrichment tools AI research agents Browser automation Cloudflare, AWS, Google Cloud, or similar infrastructure Engagement This will start as a part-time project-based role, with the potential to become ongoing if the work is strong. Estimated workload: 5 to 15 hours per week to start. To Apply Please include: Examples of automations or AI tools you have built The tech stack you usually work with A brief explanation of how you would approach automating a messy manual workflow Your hourly rate Your availability Please do not send a generic application. If your response looks copied and pasted, it will be ignored.

Colony Mobility LLC is a Florida-based technology company preparing a Phase I Small Business Innovation Research (SBIR) proposal for the U.S. Department of Transportation under Topic 26-FT1: Person-Centered, Carefree, Complete Trip Planning — Powered by AI. We are seeking a Senior AI Systems and Algorithm Researcher to serve as a named research subcontractor on this federal proposal. This is a research design and documentation role — no production software build is required. What you will research and document: Rider preference engine algorithm — design a machine learning system that learns individual traveler needs over time, including stated preferences, observed behavioral patterns, and inferred preferences for new users Success probability mathematical model — design and write a proof of correctness for a weighted scoring algorithm that calculates the probability a specific rider will successfully complete a specific trip given real-time conditions AI orchestration architecture — document the multi-agent coordination system that assembles, monitors, and replans multimodal trips in real time Outcome learning algorithm — design the reinforcement learning loop that improves system recommendations based on real trip outcomes Trip assembly algorithm pseudocode — document the step-by-step logic for building complete door-to-door journeys from multiple transportation sources LLM integration architecture — document how large language models are used within the system for normalization, preference reasoning, and conversational interfaces What the federal report specifically requires from this role: Algorithm pseudocode for all AI components Mathematical notation and proof of correctness for the success probability model Summary of how ML methods have been used to solve trip-planning problems similar to this solicitation — literature review contribution Justification of how prior research is extended and improved by this system What we need from you before July 3, 2026: A short professional bio (3–5 sentences) describing your relevant background A brief letter of commitment confirming your availability and intent to perform the described work if the contract is awarded Required qualifications: Graduate degree (Master's or PhD) in Computer Science, Applied Mathematics, Data Science, or Artificial Intelligence — or equivalent research experience Demonstrated experience designing machine learning algorithms — preference learning, recommendation systems, optimization, or routing Ability to write mathematical notation fluently — probability models, weighted scoring functions, proofs of correctness Experience writing technical research documentation — academic papers, federal research reports, or technical deliverables for a non-technical audience Familiarity with large language models and their practical limitations in production research contexts Strong plus (not required): Background in multimodal routing algorithms, operations research, or transportation optimization Experience with reinforcement learning or multi-agent systems Prior SBIR, federal research, or government contract experience Published research on routing algorithms, preference learning, or mobility AI Contract details: Hours: 180 hours over 6 months Rate: $120/hour Location: fully remote Start date: September 2026, upon DOT SBIR Phase I award notification Total contract value: $21,600 plus 20% overhead = $25,920

Project Overview We are building an AI-powered voice receptionist agent for a dental practice client. The agent will handle inbound calls 24/7 — booking, rescheduling, and canceling appointments, answering FAQs, and escalating complex situations to a human. The voice layer is built in Retell.ai and the orchestration/automation flows run in n8n, with integration into a dental practice management systems. This is a hands-on build role, not consulting. You will design, configure, and iterate the agent working directly with our founding team. What You'll Build: Conversational voice flows for new and returning patients (booking, rescheduling, cancellations, FAQs, insurance questions, urgent/emergency triage) Clean escalation paths to human staff when the agent can't handle the call n8n workflows connecting the voice agent to the practice management API for real-time schedule reads/writes HIPAA-conscious configurations and tool choices throughout the stack Performance tracking and iterative prompt/flow improvements based on real call feedback Seeking an Expert Who: You've built multiple production voice agents, ideally in healthcare or dental, and can own the full stack from architecture to delivery. You think critically about latency, barge-in behavior, edge cases, and HIPAA compliance — not just happy-path flows. Requirements: Deep expertise in Retell.ai — prompt tuning, latency optimization, interruption/barge-in handling, LLM selection, and cost modeling Advanced n8n automation — complex branching logic, dynamic data handling, external API integrations, and error recovery Experience with dental or healthcare organizations — you understand how practices operate, what front desks actually deal with, and how to translate that into agent logic HIPAA awareness — you know which tools and configurations are appropriate for PHI workflows Track record of full-cycle delivery: scoping → build → pilot → iteration Nice to have: Dentrix Ascend integration, NexHealth, prior AI receptionist or appointment-booking agent for a clinic/practice. 📋 To Apply, Please Answer Have you built a production voice agent with Retell.ai? If yes, briefly describe it. What's your approach to managing Retell.ai per-minute costs on an inbound call agent? Have you worked with dental or healthcare clients before? What was the workflow? Paste a relevant n8n workflow screenshot or describe your most complex flow.

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 seeking an AI expert to set up segregated isolated AI system environments for business lines of inquiry and workflow. The ideal candidate will have experience with Ubuntu, Proxmox, and Multi V. Responsibilities include designing and implementing secure AI environments, ensuring system integrity, and optimizing performance. The role requires strong problem-solving skills and the ability to work independently.

We are a growing construction law firm seeking an experienced AI strategist and solutions architect to advise our AI Council and help us identify, prioritize, and build practical AI solutions that improve productivity, reduce administrative burden, and enhance legal workflows. This is not a purely advisory role and it is not a traditional developer position. We are looking for someone who can help us evaluate opportunities, recommend technology solutions, perform build-versus-buy analyses, and then assist in building approved AI agents, workflows, and automations. Our initial focus is internal productivity and workflow improvement rather than client-facing applications. ABOUT US: We are a boutique construction law firm with attorneys and staff across multiple states. We have an active AI Council and are committed to thoughtful, practical adoption of generative AI. Current technology stack includes: * Microsoft 365 Copilot * Microsoft Teams * SharePoint * NetDocuments * Centerbase * Lexis Protégé Available: * Microsoft Power Platform * Power Automate * Copilot Studio * Power BI WHAT WE NEED: We are seeking a long-term advisor who can serve both in an advisory and developmental role: Strategic Advisory * Evaluate AI use cases submitted by our AI Council * Recommend practical implementation approaches * Perform build-versus-buy analyses * Evaluate AI vendors and emerging technologies * Advise on legal-industry AI trends and best practices * Help establish governance and implementation standards Solution Design & Development * Design AI agents and workflow automations * Build approved solutions within the Microsoft ecosystem * Create proof-of-concepts and pilot programs * Integrate solutions with existing firm systems where appropriate * Assist with testing, refinement, and deployment Initial Project Focus: Our first major initiative will likely involve evaluating whether we can replace or replicate portions of commercial contract review software through an internally developed solution. We are particularly interested in exploring: * Contract review workflows * Clause analysis * Risk identification * Contract summaries * Knowledge management integration IDEAL QUALIFICATIONS * Experience working with law firms, legal departments, or legal technology companies * Experience implementing generative AI solutions in professional services environments * Deep understanding of Microsoft Copilot ecosystem * Experience with Copilot Studio, Power Platform, and workflow automation * Ability to communicate effectively with attorneys and non-technical stakeholders REQUIRED * Experience designing and implementing AI agents or workflow automations * Ability to evaluate business requirements and recommend appropriate solutions * Strong understanding of current generative AI tools and trends * Ability to translate strategy into practical implementation Please provide a cover letter with your qualifications and answers to the following questions: 1. Describe a law firm or legal department AI project you personally led. 2. What is your experience with Microsoft Copilot, Copilot Studio, Power Platform, and Power Automate? 3. Describe a build-versus-buy recommendation you made and the factors considered. 4. If asked to replicate a significant portion of a contract review platform internally, how would you approach the project? 5. How do you measure ROI and success for AI implementations? We are looking for a practical advisor and builder who can help us make sound decisions, avoid unnecessary spending, and implement solutions that deliver measurable value.

I am building a daily AI voice call service called Still Here for seniors. Every morning, a warm familiar voice calls each member by name, remembers what they said last week, and has a real conversation — not a check-in, not a menu, not a script. A genuine morning conversation. All conversation scripts are already written. I provide everything. I need technical setup only. WHAT I NEED: • Retell AI account configured with HIPAA BAA signed • Florida phone number purchased and connected • Warm natural voice selected (must not sound robotic or rushed) • 7 conversation flow pathways built from my written scripts • Scheduled daily outbound calls at member-specified times • Call transcripts emailed after each call • Missed call alert system • Airtable integration for member management • 1-hour handover call — train me to manage everything going forward. CRITICAL FOR THIS PROJECT: Latency and voice warmth matter more than anything else. The person on the other end is a senior who may be lonely or uncertain. An awkward pause or robotic response breaks everything we are building. You must have experience with warm, natural outbound conversational agents — not just inbound IVR systems or chatbots. PLEASE INCLUDE IN YOUR PROPOSAL: • Your Retell AI experience specifically — projects completed • A phone number I can call to hear one of your Retell AI builds • Your approach to minimizing latency for natural conversation • Your estimated timeline • Confirmation you will sign an NDA before project details are shared A signed NDA is required before any Still Here materials are shared.

Role Overview You are the Executive AI Enablement Lead at AIVC, the person whose job is to make the executives at AIVC’s client businesses true power users of Claude, Cowork, and code- and agent-driven workflows. AIVC partners with operator businesses to drive AI-led EBITDA growth, and part of that work is bringing each company’s most senior leaders up the AI curve. You’re the person who personally designs and runs that path on every engagement: assessing where a given client executive is today; curating the right materials, videos, and course content; running 1:1 coaching; building executive playbooks; and acting as their daily operator-in-the-loop until the new workflows stick. The first concrete instance is already lined up, a named client managing partner has explicitly asked for the fastest path to becoming a power user of Claude, Cowork, and Claude Code / Skills. From there you scale: same treatment to additional client executives across the portfolio, then a documented set of executive-grade playbooks and patterns that compound across every future engagement. You are bias-toward-results – a win is the client executive’s calendar-week looking different, not a beautifully written rubric nobody uses. What You’ll Own (Outcomes) • Within 30 days of pairing with the first client managing partner, they have a working daily routine in Claude, Cowork, and Code/Skills that’s already replacing or improving how they handle at least three recurring tasks • Within the first quarter of the engagement, the client executive is a true power user — running multi-step workflows, custom Skills/Projects, and agent-assisted tasks without needing coaching scaffolding for the basics • A documented set of executive playbooks (research, writing, analysis, synthesis, workflow automation, agent-assisted tasks) that compound across every client engagement, not one-offs • A curated, current library of learning materials, videos, example workflows, and Claude-native patterns — including a clear point of view on which external courses, tutors, or expert resources are worth plugging in • Observable change in how client executive cohorts use AI: from reactive chat to repeatable, structured, outcome-oriented workflows • A foundation of training assets and patterns that scales beyond executive coaching into broader client teams in year two • A reputation among AIVC’s clients as the trusted go-to for “how do I do this better in Claude” — measured by inbound demand and engagement expansion What You’ll Do (Responsibilities) • In the first weeks: build the first client managing partner’s tailored upskilling plan — assess current usage, identify the highest-leverage workflows for their day-to-day, curate the right mix of materials / videos / course content, and recommend any tutor or expert-guided support to fold in • Provide 1:1 coaching for client executives — managing partners, founders, C-suite leaders — on Claude, Cowork, and code- and agent-based workflows • Design tailored training plans per executive that go beyond basic onboarding into advanced usage, with explicit progression from chat → workflows → agents • Curate the best external materials (videos, courses, blog posts, example projects) and rewrap them into client-ready, AIVC-flavored learning paths • Teach practical, high-leverage use cases live: research, writing, analysis, synthesis, workflow automation, and agent-assisted tasks • Help client executives move from general chat usage into repeatable workflows — Claude Projects, Skills, scheduled Cowork tasks, MCP integrations, custom agents • Serve as a real-time tutor and expert resource for client executives — over Slack, in meetings, on-site, and in async written feedback • Run office hours, workshops, and informal Q&A sessions inside client teams to keep adoption sticky between coaching sessions What We’re Looking For (Required) • Deep hands-on expertise with Claude across every surface (Claude.ai, Claude Projects, Claude Code, Claude Skills, Claude API) — and an active habit of pushing the edges of each • Strong working fluency with Claude Cowork specifically, including scheduled tasks, connected apps / MCPs, and the broader workflow surface • Strong capability with code-enabled AI workflows: you can write Python and/or TypeScript, build agents, configure MCP integrations, and ship a working internal automation end-to-end without needing an engineer • Demonstrated ability to teach non-technical but highly demanding users — you’ve made executives, founders, or senior operators meaningfully better at something complicated, not just trained engineers • Strong workflow design instinct — you can translate messy business questions into clean prompts, workflows, and systems • Polished, discreet, and effective in high-touch client executive settings — high EQ, low ego, comfortable representing AIVC inside senior client environments and around senior decision-makers • Strong bias toward practical results over theoretical AI knowledge — the metric is the client executive’s behavior change, not the elegance of the explanation • Excellent written and verbal communication; you can write a playbook a client executive will actually read and use • Comfort with significant travel to client sites and embedded, on-site engagement work • 5+ years of professional experience across some mix of: applied AI / ML, technical training and enablement, developer relations, solutions engineering, executive coaching, management consulting, or chief of staff / senior operator roles to executives Helpful If You Have (Preferred) • Prior experience coaching or supporting C-level executives, founders, or managing partners as a client-facing professional — executive coach, principal solutions engineer to executive customers, chief of staff to a CXO, or partner-level consultant • Background that combines technical depth with people skills — developer relations, solutions engineering, technical training, or learning & development at a frontier AI or developer-tools company • Direct experience building executive-facing training programs or curricula that demonstrably moved adoption inside other organizations • Hands-on familiarity with the Anthropic product surface specifically: Claude Projects, Claude Skills, Claude Code, MCP server development, Claude API • Track record of getting non-technical users to genuinely adopt a technical tool — i.e., users who chose to keep using it after the training ended • Background in management consulting, professional services, executive coaching, or learning & development — especially in environments where the customer was a senior external client • An active personal portfolio of AI work (workflows, automations, blog posts, talks, open-source contributions) you can point to • Comfort building light tooling (a Notion playbook system, a Claude Skills catalog, a small dashboard) without needing engineering support • Familiarity with AIVC’s model — operator business engagements, EBITDA-led measurement, and the broader compounding intelligence layer — or eagerness to come up the curve quickly

We are looking for an expert backend developer and automation engineer to extend an existing, production-grade Model Context Protocol (MCP) server and overhaul its orchestration layer. The headline correction for this project: the existing Lawfather MCP is to be retained and extended, not rebuilt. It already exposes deterministic, parameterized Playwright tools for every required county portal (District Clerk, HCSO, HCDAO) and a client database. Those backend tools are the reliable layer and are not the source of the instability this project exists to fix. The instability lives entirely in the orchestration layer — the model-driven layer that decides when and how to call the tools. The fix is to move deterministic control out of model-followed prose and into code, and to host the agent on an always-on machine with persistent memory. Core Project Principles • Extend, Don't Rebuild: Retain and extend the existing MCP; do not re-implement portal scrapers from scratch. • Code Over Prompts: Deterministic logic lives strictly in tool code, never in instructions the model must remember each session. • No Caller Loops: Batch operations must run to completion server-side. No operation may require the caller (model) to loop. • Agnostic Architecture: The system must remain model-agnostic and host-agnostic. No single provider — Anthropic, OpenAI, Z.ai/GLM, or Nous — may be a hard dependency. • Privilege First: Client data stays on owned hardware; the model is never the gatekeeper of which case a file belongs to. Existing Tool Inventory (To Be Inherited As-Is) The following tools already exist on the production MCP (containerized on a local Synology NAS) and are in daily use. Re-deriving their behavior is completely out of scope: • hcdc_get_docket: Court settings by date range + bar number (District Clerk). • hcdc_check_filings: Per case: standard defense filings present vs. missing. • hcdc_download_filings: Images-tab documents: bulk OR selective by filters; dest_subfolder; dry_run. Note: The parameterized download tools already cover most retrieval requests. "All filings," "this filing," "all subpoenas," "all resets," and "everything filed that day" are argument combinations on this tool, not separate features. • hcso_locate: Defendant custody location (facility / floor / pod) by SPN. • hcdao_grab_file: Download a single named file from the DA portal Files tab. • hcdao_download_discovery: Batch / delta discovery download from the DA portal. • hcdao_download_media_alert: Batch-download files listed in a 'New Media Available' portal email. • hcdao_case_summary: Scrape the Case Jacket quick summary / DAO narrative. • hcdao_plea_offer: Scrape current plea offer + full offer history. • hcdao_assigned_ada: Assigned ADA name / email / phone on a case. • lookup_client / list_clients: Resolve / list clients from the shared client database. Scoped Work (Paid Deliverables) 1. County Case Resolver (New Tool): Find a case from partial identifiers — any subset of (name, SPN, DOB, court, cause). Searches county systems (not just the local client DB). MUST return a ranked candidate list for the user to choose from; MUST NEVER auto-select. Wrong-defendant selection is a privilege failure, not a cosmetic bug. 2. Latest-Version Retrieval: Add scope=latest to hcdao_grab_file so 'most recent' selects the newest among supplements instead of the first match. 3. Async Transcribe Tool (Skill to Tool Promotion): Build a deterministic MCP tool using Gemini 3.1 Pro Preview for transcription, followed by a second pass that sends the transcript back with case context for cleanup (speaker mapping, defense-moment preamble). Long-running: implement as an async job (submit to job id to poll to fetch), NOT a synchronous call. 4. OCR Tool (Skill to Tool Promotion): Implement a readability check on ingest. If a document is not cleanly readable, FLAG it and ASK before sending to Gemini 3.1 Pro Preview for OCR. OCR must be gated and confirmed, never automatic. 5. Server-Side Batch Jobs: Move all chunk, loop, delta, and throttle logic OFF the caller and INTO the tool code. One call runs the batch to completion. 6. Queued HCDAO Fixes: For hcdao_download_discovery, add a portal_ids filter for targeted single-file pulls and a custom output-path / Drive-folder destination feature. Known Portal Quirks to Handle from Day One • hcdc_get_docket returns a broader date range than requested; results must be filtered to the requested window. • hcdao_download_discovery delta detection is blind to files organized into dated subfolders and must be explicitly handled. • Court DG7 does not surface through standard bar-number docket lookup and requires separate handling. • The Playwright Node.js driver subprocess can die silently while database tools respond; you must health-check the driver proactively. Orchestration, Host Layer, & Deployment Topology • Target Host: Hermes Agent (Nous Research) running as the persistent shell, providing persistent memory, the scheduler, and messaging surfaces. The MCP server will plug directly into it. • Agnostic LLM Routing: Default the agent/dispatch role to the most reliable tool-calling model (currently Claude Opus). Route bulk, non-critical generations (draft summaries, transcript cleanup) to a cheaper model (e.g., GLM-5.2). No provider may be hard-wired. Per-tool pins are allowed strictly for transcription/OCR tasks (pinned to Gemini 3.1 Pro Preview). • Memory Fencing: Hermes's persistent memory and learning loops must remain enabled to accumulate facts and user preferences. However, the agent must be strictly fenced from self-editing or rewriting its own mechanical execution paths (portals, downloads, filings), which must remain frozen in MCP tool code. • Hardware Deployment Infrastructure: • Always-on Brain: M1 Pro MacBook Pro (16 GB, mains-powered, lid open) running the Hermes gateway, Messages.app, and a BlueBubbles iMessage bridge. Must be fully automated via launchd services to handle headless crash recovery, auto-login, and sleep prevention (pmset autorestart / caffeinate). • Tools and Storage: Synology NAS (10.0.0.149) hosting the Lawfather MCP container, local client folders, and Drive sync. • Private Network: Tailscale mesh across all devices for secure remote access without open inbound ports. Acceptance Criteria for Sign-Off • No batch operation requires the caller to iterate. • The case resolver returns ranked candidates and never auto-selects. • Transcription runs seamlessly as an async two-stage job surviving multi-hour files without timing out. • OCR never fires automatically on low-readability files without gated confirmation. • Zero regressions on the existing MCP tool inventory. • The Resiliency Test: The full stack successfully restarts completely unattended after a host reboot or simulated power loss, and is reachable via iMessage/SMS immediately after. • Self-editing is fenced on mechanical download/filing paths. Hard Guardrails • Privilege: Downloads route strictly to the correct client folder; a wrong-case match is treated as a severe defect, not a warning. Privileged audio/discovery data stays on owned hardware where the chosen model allows. • Determinism: Repeatable steps live entirely in tool code, never in prompts. • Agnosticism: Model and host layers must remain fully swappable without modifying the core MCP tools. Before quoting "done," you will be expected to confirm live portal behaviors regarding District Clerk document labels, DA portal stable identifiers, and county search surfaces. How to Apply Please submit a proposal detailing your specific experience with MCP architectures, Playwright browser automation, and macOS/Docker DevOps automation. Anti-Bot Filtering: To prove you read this entire scope, please start your application with the phrase "PROTECT THE LAW" in all caps. Automated or generic copy-paste applications will be instantly rejected.

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.