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.

We’re looking for an experienced engineer or trainer to help deliver hands-on AI coding training for engineering teams. The focus is on moving engineers beyond basic Copilot/autocomplete usage into agentic workflows with tools like Claude Code, Codex, GitHub Copilot, and related tooling. Scope: Run practical workshops for engineers Teach Claude Code, Codex, and Copilot workflows Cover task scoping, prompting, reviewing diffs, and code validation Help create assets like CLAUDE.md, agents.md, slash commands, or workflow templates Advise on best practices, cost awareness, security, and team adoption Ideal candidate: Strong software engineering background Hands-on experience with Claude Code, Codex, or similar tools Experience training engineering teams Clear communicator with practical examples Please apply with relevant examples of AI coding workflows, trainings, or engineering teams you’ve supported.

We are building a semiconductor manufacturing intelligence platform designed to help engineers rapidly identify yield excursions, investigate root causes, and capture institutional process knowledge. A working foundation already exists, including yield dashboards, lot tracking, process-route visualization, maintenance-event correlation, and investigation timelines. We are now looking for a highly capable developer to extend and refine the system into a production-grade engineering decision-support tool. This is not a basic dashboard project. The goal is to enhance an existing platform into a system that connects manufacturing data, equipment history, and engineering knowledge with lightweight AI-assisted analysis. Key Objectives Help engineers answer questions such as: * Why did yield drop? * What changed before the excursion started? * Which tools or chambers are most likely responsible? * Have we seen a similar issue before? * What corrective actions worked previously? Scope of Work Investigation Workspace * Improve investigation timelines * Correlate process events, SPC/FDC signals, maintenance activity, and yield changes * Enhance interactive debugging workflow Historical Excursion Search * Simple similarity matching using rules or embeddings/API-based methods * Retrieve past investigations and outcomes Engineering Knowledge Layer * Searchable notes, documents, and reports * Store corrective actions and process changes AI-Assisted Summaries (lightweight) * Generate investigation summaries using an LLM API * Suggest possible contributing factors based on available data Ideal Candidate * Strong full-stack or data engineering experience * Comfortable working with existing codebases * Experience with analytics dashboards or industrial systems * Familiarity with APIs, databases, and data modeling * Bonus: exposure to manufacturing or semiconductor data Notes * This is an extension of an existing platform, not a rebuild * Focus is on practical implementation rather than complex architecture * Speed and execution matter more than theoretical design * Potential for ongoing work if collaboration goes well

Seeking an experienced technical consultant with broad engineering experience and strong technical judgment. Requirements * 10+ years of experience in software engineering, solution architecture, technical consulting, engineering leadership, or related technical disciplines * Native English speaker with clear verbal communication skills * Current or recent involvement in software delivery, architecture, infrastructure, cloud, AI, automation, or enterprise technology projects * Strong understanding of software architecture, cloud platforms, APIs, integrations, data systems, and engineering practices * Ability to evaluate technical depth through open-ended technical discussions * Comfortable discussing unfamiliar technical domains and ambiguous requirements * Ability to distinguish hands-on experience from theoretical knowledge Application Requirements * Short summary of your technical background * Types of systems, technologies, and organizations you have worked with * 5-minute Loom video introducing yourself and explaining how you evaluate technical expertise during technical discussions Applications focused primarily on sourcing, recruiting, resume screening, or administrative hiring activities will not be considered.

NobleProg is seeking an experienced Claude AI Trainer to deliver instructor-led training programs for corporate clients in the Denver, Colorado area. We are looking for a trainer with both technical expertise and practical business experience using Claude by Anthropic. The ideal candidate should be equally comfortable teaching software developers and IT professionals, as well as non-technical business users who want to improve productivity and everyday workflows using Claude. Engagement Details Location: Onsite in Denver, CO (remote delivery may also be considered) Daily Rate: $1,200 per day onsite ($1,100 per day remote) Course Scope The training will introduce participants to Claude AI fundamentals, prompt engineering, responsible AI usage, and practical enterprise applications. Depending on the client’s needs, sessions may include technical workflows for developers and IT teams, as well as role-based business applications for non-technical users across various departments. NobleProg SOP: https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Key Topics Include * Claude AI fundamentals and responsible AI usage * Prompt engineering techniques and best practices * Claude for software development, coding assistance, debugging, and technical documentation * Claude for business productivity, reporting, communication, research, and document creation * Workflow automation using Claude * Building effective prompts for different business functions * AI governance, privacy, and enterprise best practices * Hands-on exercises using practical business and technical scenarios Trainer Responsibilities * Deliver engaging instructor-led training onsite or remotely * Teach both technical and non-technical audiences with confidence * Facilitate interactive workshops with demonstrations and hands-on exercises * Customize course content, examples, and exercises based on client requirements * Answer participant questions and provide practical guidance for adopting Claude in daily work * Prepare training materials (trainer retains ownership of content) * Stay current with developments in Claude and enterprise generative AI Required Qualifications * Extensive hands-on experience using Claude by Anthropic * Demonstrated experience applying Claude in both technical and business environments * Experience teaching software developers, IT professionals, or other technical audiences * Experience delivering AI training to business users and non-technical professionals * Previous experience delivering instructor-led corporate training, workshops, or professional education * Strong presentation, facilitation, and communication skills * Ability to adapt training for audiences with varying levels of technical expertise Preferred Qualifications * Experience with ChatGPT, Gemini, Microsoft Copilot, or other enterprise AI platforms * Background in AI consulting, digital transformation, or enterprise technology * Experience training corporate or government clients * Experience designing role-based AI training programs * Familiarity with prompt engineering, AI workflow automation, and enterprise AI governance How to Apply Please include: * A brief overview of your Claude AI experience * Your experience delivering corporate AI training * Examples of teaching both technical and non-technical audiences * Your availability for delivery in Denver, CO * Any relevant AI certifications, presentations, or training materials

DESCRIPTION We're a small applied AI lab running a live, production-track AI product for an institutional financial services client. The work is technical, fast-moving, and high-stakes. We need to fill a critical infrastructure role with someone senior, collaborative, and genuinely excited about building in the current AI tooling ecosystem. THE ROLE You'll own the data infrastructure layer for an AI-powered intelligence platform built on the Microsoft Azure ecosystem. This is a hands-on engineering position — you're responsible for designing, building, and maintaining the pipelines that feed a live AI scoring engine. The environment is agentic. Data moves from 15+ heterogeneous external sources (APIs, PDFs, regulatory filings, web) through Bronze, Silver, and Gold layers into a scoring and inference system. The hard problems are extraction quality, schema normalization, pipeline reliability, and getting the right data to the scoring engine in the right shape. You'll work directly with the technical lead and engagement lead. No layers. Fast decisions. WHAT YOU'LL OWN + Data pipeline architecture and delivery across Bronze (raw ingestion), Silver (normalization, NLP extraction, entity resolution), and Gold (unified output, scoring-ready) layers + Microsoft Fabric lakehouse implementation — OneLake, Data Pipelines, Dataflows Gen2, Warehouse, and downstream system integration + Microsoft Foundry (formerly Azure AI Studio) — agent orchestration, prompt pipelines, and AI model integration within a secure Azure tenancy + Azure Data Factory orchestration for structured source ingestion +Salesforce integration via Snowflake native connector — field mapping, custom object schemas, sync reliability Extraction pipelines for unstructured sources (PDFs, regulatory filings, web content), coordinating with Azure OpenAI-based extraction agents +Data governance and security posture — all data stays within the client's Azure tenancy; data residency is non-negotiable REQUIRED: Technical Skills + Microsoft Fabric — production experience, not sandbox. You should be able to speak to Lakehouse vs. Warehouse tradeoffs, OneLake architecture, and real pipeline implementation. Microsoft Foundry / Azure AI Studio — hands-on with agent deployments, prompt flow, model endpoints, and Azure OpenAI integration within an enterprise Azure tenancy + Azure Data Factory — pipeline authoring, trigger management, connector configuration, monitoring +Snowflake — Gold layer data warehousing, schema design, query optimization, native connector usage (specifically Salesforce) + Python — data engineering contexts: pandas, PySpark, API clients, extraction scripts + SQL — complex joins, window functions, schema design; SQL Server preferred + Azure Blob Storage / ADLS Gen2 — Parquet/Delta format, access control, lifecycle management REQUIRED: AI-Augmented Development This is a hard requirement. You should be actively using AI coding tools to multiply your output — fluency with Claude Code, Cursor, and OpenAI Codex as part of your daily development workflow. If these aren't already in your stack, this isn't the right fit. We hire for multiplied output, not raw hours. REQUIRED: Demonstrable Work We don't evaluate resumes alone. Bring something — a GitHub repo, a deployed pipeline, an architecture document you authored, a case study with real numbers. We should be able to look at your work and understand what you built, what decisions you made, and why. Work under NDA is fine if you can describe it in enough detail to convey complexity and ownership. ATTITUDE & WORK STYLE Comfortable with Agile Scrum and its accompanying ceremonies. You raise issues early and help solve them. You communicate tradeoffs clearly without over-explaining. You're comfortable with evolving specs and don't need to win the architecture argument — just build the right thing within the approved stack. We're a small, senior team with low friction and direct communication. That's the environment; it works if you work with it. THE STACK The client environment has specific technology approvals. Production work runs on Azure OpenAI (client-hosted), Microsoft Fabric, Microsoft Foundry, Snowflake, Azure Data Factory, ADLS Gen2, Salesforce via Snowflake native connector, and SQL Server. LangChain, DeepSeek, and the external Claude API are not approved for this environment. NICE TO HAVES Experience with financial services or institutional investment data (SEC EDGAR, public pension filings, regulatory documents), familiarity with InvestorFlow or Salesforce Financial Services Cloud, unstructured document extraction at scale, or Azure Purview.

Seeking an experienced technical consultant with broad engineering experience and strong technical judgment. Requirements * 10+ years of experience in software engineering, solution architecture, technical consulting, engineering leadership, or related technical disciplines * Native English speaker with clear verbal communication skills * Current or recent involvement in software delivery, architecture, infrastructure, cloud, AI, automation, or enterprise technology projects * Strong understanding of software architecture, cloud platforms, APIs, integrations, data systems, and engineering practices * Ability to evaluate technical depth through open-ended technical discussions * Comfortable discussing unfamiliar technical domains and ambiguous requirements * Ability to distinguish hands-on experience from theoretical knowledge Application Requirements * Short summary of your technical background * Types of systems, technologies, and organizations you have worked with * 5-minute Loom video introducing yourself and explaining how you evaluate technical expertise during technical discussions Applications focused primarily on sourcing, recruiting, resume screening, or administrative hiring activities will not be considered.

Type: Hourly, ongoing (part-time to full-time, room to grow) Stack you'll work in: Notion, Slack, HubSpot, Google Workspace/Gmail, Claude + other LLM APIs, Zapier/Make/n8n About us We're a fast-moving sports and fan-engagement startup. We're small, we ship quickly, and we want AI woven into how the whole company operates, not as a side experiment, but as the default way we work. You'd be the person who makes that real. What you'll do Map our current workflows across sales, marketing, ops, and content, then find the highest-leverage places to automate. Build automations and agent workflows that connect our tools (Notion, Slack, HubSpot, Gmail/Google Workspace) using platforms like Zapier, Make, or n8n plus LLM APIs. Design and ship AI agents for real jobs: lead routing and CRM enrichment, content drafting, customer/fan response triage, internal knowledge search, reporting digests. Stand up the connective tissue (prompts, integrations, guardrails, and monitoring) so automations are reliable, not brittle demos. Train and enable our team: build SOPs, run working sessions, and create lightweight docs so non-technical people actually adopt what you build. Help set our AI strategy and roadmap as we scale. You're a strong fit if you Have shipped real automations and AI agent workflows in production (not just prototypes). Are fluent with Zapier / Make / n8n and at least one major LLM API (Anthropic/Claude, OpenAI). Know your way around HubSpot, Notion, Slack, and Google Workspace integrations and APIs. Can write clean prompts and think in systems: edge cases, error handling, human-in-the-loop checkpoints. Can explain technical work to non-technical people and get them to adopt it. Communicate proactively and move fast without breaking trust on things that touch customers or revenue. Nice to have Experience taking a small company "AI-native" end to end. Background in sports and/or blockchain. Comfort with light scripting (Python/JS) when no-code hits its limits. How to apply In your proposal, please: Describe one AI agent or automation you built, the tools involved, and the measurable result. Tell us how you'd approach training a non-technical team to actually use what you build. This part matters as much as the build. Share your hourly rate and weekly availability. Proposals that skip these will be passed over. We're looking to start with a small paid task and grow the engagement from there.

Conversational AI / LLM Consultant We are looking for a Conversational AI and LLM specialist to support the strategy, design, development, testing, and improvement of AI-powered chatbot and voice automation solutions across multiple business groups. Responsibilities: Help identify, evaluate, and prioritize Conversational AI and LLM use cases across defined business units. Advise on best practices for Conversational AI strategy, LLM architecture, prompt design, orchestration, retrieval, integrations, and development. Recommend improvements across AWS services, Amazon Lex integrations, LLM workflows, and supporting AI infrastructure. Collaborate with the development team on chatbot, voice bot, Lex, and LLM-based implementations and configurations. Conduct QA testing to validate Conversational AI functionality, accuracy, performance, reliability, and user experience. Support the development of solution frameworks, automation workflows, dashboards, application management tools, and fulfillment processes. Assist in designing and extending multilingual Conversational AI solutions in English and Spanish. Support multiple lines of business, call flows, customer journeys, and AI-assisted workflows. Ideal Candidate: Experience with Conversational AI, LLMs, and chatbot or voice automation systems. Familiarity with Amazon Lex and AWS AI services is helpful, but broader LLM architecture experience is equally important. Strong understanding of prompt engineering, AI orchestration, integrations, QA testing, and production AI workflows. Ability to translate business requirements into practical AI-driven solutions. Experience with multilingual conversational design, especially English and Spanish, is a plus.

I’m running a real estate investment platform called ToInvested.com. The project is about 90% finished, and most of the code was built with Claude together with another engineer. Now I need a senior engineer to step in, review the full product carefully, test every major workflow, and help verify that everything is working correctly before it goes live. This is not just a “write more code” role. I need someone who can look at the platform like a real product, find hidden bugs, catch weak logic, test edge cases, review the AI-generated code, and tell me honestly what is ready and what still needs fixing. Because this is a real estate investment platform, accuracy and trust matter a lot. Users may rely on property data, investment logic, calculations, and AI-driven insights, so even small issues can create a serious problem later. The ideal person has strong full-stack experience, understands AI-assisted development, and has a good testing mindset. Real estate tech experience would be a big plus, especially with property platforms, investment tools, marketplaces, mortgage systems, or financial workflows. My main goal is simple: I want someone to break the project before real users do. If you’re the kind of engineer who can take a nearly finished product, test it deeply, clean up weak areas, and help make it production-ready, I’d be happy to talk.