About the Role: We are seeking a highly qualified Senior Machine Learning and Natural Language Processing Engineer with deep expertise in sentence parsing, contextual understanding, categorization, and language extraction to support and advance Sybal’s Proof of Governance® (PoG™) platform. This role blends advanced NLP engineering, full-stack development, and enterprise-grade deployment. You will design custom NLP models, build scalable AI-driven services, and deploy production-ready applications that transform raw policy and technical language into structured governance intelligence. You must be a senior-level full-stack engineer proficient in Python, Django, JavaScript, HTML, and CSS, with the ability to dockerize and deploy applications into production environments. Experience commercializing enterprise AI applications is required. You should also be familiar with using agentic AI tools in a development context—for debugging, workflow acceleration, rapid prototyping, and improving engineering efficiency. ________________________________________ Key Responsibilities: NLP & Machine Learning Engineering: • Build advanced NLP models for sentence parsing, context detection, semantic analysis, entity extraction, and policy language interpretation. • Develop hybrid ML + rule-based systems that support governance modeling and policy decomposition. • Create pipelines for text ingestion, annotation, categorization, and structured language extraction. • Design evaluation frameworks for accuracy, drift, reliability, and linguistic precision. • Research and implement non-LLM NLP methods relevant to governance and policy analysis. Full-Stack Engineering: • Develop production-ready applications using Python (spaCy, NLTK, TensorFlow, or PyTorch to build and optimize NLP models), Django, JavaScript, HTML, CSS, and modern tooling. • Further develop NLP models for PoG™ Feature enhancements. • Develop and maintain secure, scalable REST APIs and backend services. • Integrate ML components seamlessly into PoG™’s architecture. Production Deployment & DevOps: • Dockerize machine learning pipelines and full-stack applications for uniform deployment. • Deploy and manage services in cloud production environments (AWS, GCP, or Azure). • Set up CI/CD pipelines, monitoring, observability, and scalable containerized processes. • Ensure production performance, uptime, and system reliability. AI Automation for Engineering Efficiency: • Use agentic AI tools to assist with debugging, test generation, workload orchestration, and internal development workflows. • Integrate AI-assisted coding tools responsibly into engineering processes. Contribute to the Proof of Governance® Platform: • Build NLP and ML components that strengthen PoG™’s ability to: • Map policy language into structured governance data • Detect enforceability gaps • Identify policy dependencies and contextual interactions • Deliver measurable, enforceable governance intelligence • Collaborate with PoG™ architects to extend platform intelligence across governance domains. ________________________________________ Qualifications: Required Skills & Experience: • 6–10+ years of software engineering experience with specialization in ML and NLP. • Mastery of sentence parsing, syntax/semantic analysis, dependency modeling, and contextual extraction. • Proven experience commercializing enterprise AI or ML-driven applications. • Proficiency in: o Python o Django o JavaScript o HTML / CSS • Demonstrated ability to dockerize applications and deploy them into production. • Strong understanding of ML architecture, data modeling, distributed systems, and backend engineering. • Experience using agentic AI tools for engineering workflows (debugging, code analysis, test generation). • Strong cloud engineering experience (AWS, GCP, Azure). Preferred Qualifications: • Background in computational linguistics or structured policy analysis. • Experience with ontologies, taxonomies, or governance modeling. • Prior work in regulated, audit-heavy, or mission-critical environments. • Contributions to high-scale enterprise software platforms. ________________________________________ Who You Are: • You excel in both advanced NLP engineering and full-stack software development. • You can design systems end-to-end—from custom algorithms through front-end integration to production deployment. • You understand how to use AI to accelerate development processes. • You are driven by building systems that transform governance from assumption to measurable, enforceable proof. • You are excited to contribute to the continuous evolution of PoG™

We are an outsourced accounting and tax firm serving small businesses. We handle bookkeeping, accounts payable, expense management, financial reporting, and tax preparation. We're at a point where we want to seriously rethink how we work — not just add AI tools on top of old processes, but redesign our workflows from the ground up to take advantage of what AI can actually do. We're looking for a contractor who has done this before — someone who understands accounting and tax workflows deeply and also has hands-on experience implementing AI tools in professional services or finance environments. What you'll do: • Map our current accounting and tax workflows end-to-end • Identify where AI can replace, assist, or accelerate steps • Research and evaluate tools that fit our stack (we use QBO, and are open to others) • Work alongside our team to build and document the new workflows — not just hand us a deck • Help us implement and iterate until the workflows are actually running Who we're looking for: • Strong background in accounting, tax, or working closely with firms that do this work • Real experience deploying AI tools in a professional services or finance context — not just theoretical • Familiarity with tools like AI document processing, workflow automation, or AI-assisted review • Someone who can roll up their sleeves and build alongside us, not just consult from a distance This is a part-time engagement to start. If it goes well, there's room to expand the scope. We are serious about this — we want to move from buying tools to actually redesigning how we operate, and we need someone who has done it. In your application, please tell us: what accounting or tax workflows have you redesigned with AI, what tools were involved, and what the before/after looked like. Specifics matter more than credentials here.

Overview I have a Next.js website with a newsletter signup form that currently submits directly from the browser to HubSpot's Forms v3 endpoint. I want to add a lightweight LLM-based spam filter that inspects each submission *before* it reaches HubSpot, and silently rejects (or flags) anything that looks like spam/bot/junk input. Current setup - Framework: Next.js (App Router, TypeScript, React client component) - The form component (`NewsletterForm.tsx`) POSTs directly to `https://api.hsforms.com/submissions/v3/integration/submit/[portalId]/[formGuid]` - Fields collected: `firstname`, `lastname` (optional), `jobtitle`, `email` - Portal ID and Form GUID are public form identifiers (no secrets today) What I want you to build 1. Create a server-side API route in the Next.js app (e.g. `app/api/subscribe/route.ts`) that: - Receives the form fields from the client - Runs an LLM spam/quality check (e.g. OpenAI or similar) to classify the submission as legit vs. spam — checking for gibberish names, fake/disposable emails, nonsense job titles, injection attempts, etc. - If legit → forwards the submission to HubSpot (server-side) - If spam → rejects gracefully with a generic message (no HubSpot write) 2. Update the existing `NewsletterForm.tsx` to POST to the new internal API route instead of calling HubSpot directly. 3. Keep the LLM API key server-side only (use an environment variable — never expose it to the client). 4. Preserve the existing UX: loading / success / error states should still work. Deliverables - Working API route with the LLM spam check + HubSpot forwarding - Updated form component - Brief note on which env vars to set (`OPENAI_API_KEY`, etc.) and how to configure them - Clean, typed TypeScript that matches the existing code style Nice to have (optional) - Basic rate limiting / honeypot field as a cheap first line of defense before the LLM call - Configurable spam threshold or a logged "reason" when something is rejected Requirements to apply - Strong Next.js App Router + TypeScript experience - Experience calling an LLM API (OpenAI or equivalent) from a server route - Familiarity with HubSpot Forms API is a plus To apply, please briefly answer: 1. Which LLM/provider would you use and roughly what would it cost per submission? 2. How would you handle the case where the LLM API is slow or down — do you fail open (let it through) or fail closed (block it)? 3. Have you integrated with HubSpot Forms before? (yes/no is fine)

This is a collaborative role where you'll help create high-quality AI avatar videos, short-form reels, and social media content while documenting the process into reusable SOPs and prompt libraries. ***All content developed will be used toward our portfolio to generate new clients More info: You'll work directly with me to create AI avatar videos, short-form content, and social media reels using tools such as HeyGen, Synthesia, Higgsfield, Runway, Veo, CapCut, ElevenLabs, or similar AI platforms. You should have a strong eye for storytelling, editing, and content that performs on social media while being comfortable experimenting with new AI tools and workflows. You'll help develop portfolio-quality projects, fulfill client content requests, test and optimize AI production processes, document repeatable workflows into clear SOPs, and build an organized library of prompts for different content styles and use cases. I'm looking for someone who is reliable, organized, communicates well, enjoys collaborating, and is interested in helping build a scalable AI content production system rather than simply editing videos. Some content examples: https://www.instagram.com/reel/DX8DyhkNt7H/?igsh=MWsweW03NTZleDRodg== https://www.instagram.com/reel/DZn61OPxWjr/?igsh=MTc4aGtpeGtybmg3eQ== https://www.instagram.com/reel/DZaOvs6RuLX/?igsh=MWVlOHlyaHlza2NuaQ== https://www.instagram.com/reel/DZigU_CSbw5/?igsh=d3c0cnk3d2hndGdm https://www.instagram.com/reel/DXE6h0eEd0o/?igsh=ZDgzemRwbjd2Njgw https://www.instagram.com/reel/DZV4YDlqoJn/?igsh=MXVxNHUyM3VscDAxdw== https://www.instagram.com/reel/DXP4WCIgLcp/?igsh=OTNhcWZ0dnB3Z210 Initial Project Budget: Phase 1 – Portfolio Development & Coaching Budget: $1,000 - Develop portfolio-quality content together. - Build repeatable workflows. - Refine creative direction. Phase 2 – SOP Development, Service Fulfillment Testing & Prompt Library - Budget: $500 - Document production workflows. - Create standardized SOPs. - Build an organized prompt library. - Test and optimize fulfillment processes for client delivery. Opportunity: This can become an ongoing role as we begin producing content for paying clients. I'm looking for someone who wants to help build the production system—not just edit videos.

AI SYSTEMS ENGINEER Agentic AI, Multi-Agent Systems & Secure AI Workflows (U.S.) Remote • United States We're building production AI systems designed for enterprise environments. We're looking for exceptional AI systems engineers who enjoy solving difficult systems problems – not just writing code. Our work sits at the intersection of agentic AI, software architecture, enterprise systems, governance, security, and operational intelligence. We design AI systems that improve how organizations operate while meeting the standards required for production deployment. We value engineers who think in systems, challenge assumptions, and care deeply about building technology that is reliable, understandable, secure, and useful. If you're motivated by difficult engineering problems, thoughtful architecture, and building production AI systems for enterprise organizations, we'd like to hear from you. WHAT YOU'LL HELP BUILD Examples of the types of systems we design include: - Multi-agent AI systems - Enterprise AI assistants - Secure AI workflows - Enterprise workflow automation - AI-powered knowledge systems - Human-in-the-loop decision support - Document intelligence - Retrieval-Augmented Generation (RAG) - AI memory and retrieval systems - AI evaluation and testing frameworks - Secure enterprise AI platforms - AI governance capabilities - Operational intelligence platforms TECHNICAL EXPERIENCE WE VALUE We're interested in engineers with experience in some combination of: - Python - AI Agent Development - LangGraph - LangChain - Large Language Models - API Development - Vector Databases - Software Architecture - Enterprise Systems Integration - Information Security Experience with OpenAI, Anthropic, Model Context Protocol (MCP), cloud infrastructure, workflow orchestration, observability, distributed systems, or regulated technology environments is also valuable. We do not expect expertise in every technology. We care far more about engineering judgment, systems thinking, demonstrated execution, and continuous learning than checking every technology box. THE PROBLEMS WE ENJOY SOLVING The engineers who thrive here enjoy questions like: - How should multiple AI agents coordinate work? - How should humans remain in control of important decisions? - How should production AI systems scale safely? - How should memory be designed for enterprise AI? - How should AI systems balance operational performance with governance, security, and reliability? - How should AI systems create measurable business value? If those questions excite you, you'll probably enjoy working with us. WHAT MAKES SOMEONE SUCCESSFUL HERE We're looking for engineers who: - Think in systems rather than individual features. - Care deeply about production quality. - Enjoy solving ambiguous technical problems. - Communicate complex ideas clearly. - Balance speed with sound engineering judgment. - Build practical solutions rather than chasing hype. - Continuously learn, experiment, and improve. We're significantly more interested in systems you've built than technologies you've used. Please provide specific examples that demonstrate your role, engineering decisions, and measurable outcomes. We recognize that many engineers use AI as part of their workflow. You're welcome to do the same. However, your application should accurately reflect your own experience, judgment, and technical thinking. We respect the confidentiality of your current and former employers, clients, and partners. Please do not include proprietary or confidential information in your application. Describe your work at a level that demonstrates your engineering approach without disclosing protected information. PROFESSIONAL STANDARDS We value integrity, sound engineering judgment, and respect for intellectual property. Please do not include confidential, proprietary, export-controlled, or other non-public information belonging to your current or former employers, clients, or partners in your application or work samples. We're interested in your engineering approach, architectural thinking, and problem-solving methodology – not protected information belonging to others. If you share code, architecture diagrams, technical documentation, or project examples, please ensure you have the legal right to do so and identify any material open-source or third-party technologies where appropriate. By submitting application materials, you represent that you have the legal right to share them and that doing so does not violate any confidentiality, intellectual property, employment, consulting, or other contractual obligations. Any engagement, if offered, will be subject to a separate written agreement covering confidentiality, intellectual property ownership, compensation, and other applicable terms. Submission of an application or participation in the evaluation process does not create any employment, independent contractor, partnership, joint venture, agency, fiduciary, or other business relationship with 26ers AI, nor does it obligate either party to enter into any future engagement. 26ers AI reserves the right to evaluate applications, discontinue discussions, modify the hiring process, or decline to pursue any engagement at its discretion. Nothing in this posting should be construed as an offer of employment or an offer to contract.

We're seeking a skilled Full Stack Developer to join our team. You'll debug and resolve errors within our applications, support improvements, troubleshoot issues, and integrate our apps with our CRM system. Just as important: you'll help keep our GHL-based AI agent and voice AI systems running smoothly for live clients. Read the whole post before applying. We've written it to be direct on purpose. If any of it doesn't sound like you, that's okay , please self-select out. We'd rather know now. What we're looking for We need a contributor, not just a doer. That means someone who takes initiative, spots issues before they become problems for the client, and drives toward solutions instead of waiting to be told what to do next. If you're the type who finds the answer rather than asking us for it, you'll do well here. If you need your hand held on every step, this isn't the right fit. We work with live clients on voice AI and AI agents, so being proactive and responsive is not optional. When something breaks or drifts, we need you evaluating, fixing, and improving it, thinking forward about the end-user experience, not just reacting after a client complains. Responsibilities • Debug and provide solutions for errors in web and mobile applications.. • Support ongoing improvements and optimizations.. • Troubleshoot technical issues efficiently, ideally before they reach the client.. • Integrate applications with our CRM system (GoHighLevel / GHL) for seamless functionality.. • Connect systems and third-party services via APIs to enhance platform capabilities.. • Support, adjust, and maintain GHL AI agents and voice AI workflows so they perform reliably for clients.. Requirements • Proven experience as a Full Stack Developer.. • Strong proficiency in front-end and back-end technologies (e.g., JavaScript, React, Node.js, Python, etc.).. • Experience with GHL CRM integrations.. • Working knowledge of and comfort with GHL AI agents and automations — you don't need to be a specialist, but you should be able to get in, understand how they're built, make adjustments, and pick things up quickly.. • Familiarity with or genuine willingness to dig into voice AI. We expect everyone on the team to be able to work with it.. • Proficiency in designing and implementing REST-based API connections.. • Excellent problem-solving and debugging skills.. • Ability to work independently, take initiative, and meet deadlines.. How we work • We're engaging you — not your agency. The person we hire is the person doing the work. If you subcontract or hand work to others, this isn't a fit.. • Short leash, high trust. Everyone starts on a trial basis. We give people room to learn, but you'll need to learn fast, dive in, and prove it quickly. If you're trying to learn the fundamentals on the job, this role isn't for you.. • Own your outcomes. Bring the thinking, not just the hands. Recognize what the end user's experience will be and drive it.. If that sounds like the environment you want, we'd love to hear from you.

I am looking for a developer or agency to build a web platform called First Responder Academy. The goal is to create the most comprehensive AI-powered training platform for firefighters, EMTs, and paramedics. The platform will initially focus on: 1. Firefighter Oral Board Preparation 2. EMT NREMT Preparation 3. Paramedic NREMT Preparation 4. Firefighter I Certification Preparation 5. Firefighter II Certification Preparation Core Features Required: * User accounts and login system * Membership and subscription management * AI-powered training simulator * Progress tracking * User dashboard * Mobile-friendly design * Modern, professional interface * Ability to scale over time AI Training Features: The AI should be able to: * Conduct firefighter oral board interviews * Score responses * Provide detailed feedback * Generate EMT and paramedic scenarios * Generate Firefighter I and II questions * Create multiple-choice exams * Provide remediation and study recommendations * Increase or decrease difficulty based on performance Knowledge Base Features: The system should allow: * Uploading NREMT skill sheets * Uploading EMS protocols * Uploading Firefighter I and II standards * Uploading department-specific hiring packets * Uploading study guides and training documents The AI should reference uploaded materials when generating questions and grading answers. EMS Features: * EMT training mode * Paramedic training mode * NREMT-style testing * Practical scenario evaluations * Protocol-based learning * Trauma and medical assessment training * Airway management training * Cardiology training Cardiology Module: A major future feature will be ECG interpretation. Requirements: * ECG image library * Random ECG presentation * Student interpretation * AI grading and feedback * Rhythm recognition training * Treatment decision evaluation Firefighter Features: * Firefighter Oral Board Simulator * Firefighter I Exam Preparation * Firefighter II Exam Preparation * NFPA / IFSAC-based content * Scenario-based learning * Multiple-choice testing * Leadership and decision-making exercises Department-Specific Training: The platform should eventually allow users to: * Upload local EMS protocols * Upload department hiring packets * Upload mission statements and values * Receive customized training based on those documents Progress Tracking: The platform should track: * User scores * Weak areas * Practice history * Completion rates * Recommended study topics * Improvement over time Admin Features: * Upload and manage training content * Upload and manage protocol documents * Upload and manage ECG images * Create and edit question banks * View user analytics * Manage subscriptions Technology Preferences: * Modern scalable architecture * AI integration using OpenAI API * Secure user authentication * Cloud-hosted * Responsive design * Easy content management Design Style: * Professional * Modern * Clean * Fire and EMS themed * Dark mode preferred * Red, black, gray, and white color palette Important: I am looking to build an MVP first, not every feature immediately. Phase 1 priorities: 1. User accounts 2. AI training simulator 3. Oral board preparation 4. EMT preparation 5. Paramedic preparation 6. Firefighter I and II preparation 7. Progress tracking 8. Membership system Future phases can include ECG image testing, advanced protocol integration, department-specific customization, and additional first responder training programs. Please provide: * Estimated timeline * Estimated cost * Recommended technology stack * Examples of similar projects * Suggestions for MVP development

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.

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)

We're building an internal operations platform to automate utility account management for a large real estate portfolio. Today, much of this work is manual. Information about utility accounts exists across multiple systems, and employees spend significant time identifying missing bills, reconciling account data, researching exceptions, and coordinating follow-up work. We're building a system that automates these processes by synchronizing data between our operational system and accounting system, applying business rules to identify exceptions, and presenting actionable work queues and dashboards for our operations team. Examples include: Utility accounts that exist in one system but not another Missing or delayed utility bills Accounts requiring setup or closure based on occupancy changes Autopay and e-bill tracking Operational exceptions that require human review Dashboards, work queues, assignments, notes, and status tracking Our internal product manager owns the business requirements and workflows. Your role is to work closely with them to design and implement the technical solution, not to perform business process discovery. What You'll Do Design and use AI to build the application's backend and frontend. Design a clean, maintainable application architecture. Use AI to build dashboards and workflows that allow operations teams to efficiently manage exceptions. Translate product requirements into production-ready software. Leverage AI development tools (Codex, Claude Code, Cursor, or similar) as a core part of your workflow to accelerate development. Review, validate, and refine AI-generated code to ensure quality and maintainability. What We're Looking For We're looking for an experienced software engineer with strong software engineering fundamentals who embraces AI-assisted development. You should understand how modern software applications are architected, designed, built, and deployed, and be comfortable making sound technical decisions while moving quickly. Experience in many of the following areas is preferred: Full-stack application development Application architecture and system design APIs and system integrations SQL databases and data modeling Authentication and security Cloud-hosted applications Testing and debugging Source control and collaborative development We care much more about engineering judgment, speed of execution, and the ability to effectively leverage AI than expertise in any particular language or framework. Nice to Have Experience building internal business applications or operations platforms Experience working with accounting, ERP, or workflow systems Experience building dashboards and operational tooling To Apply Please include: A brief summary of your experience building business applications. The AI development tools you use regularly (Codex, Claude Code, Cursor, Windsurf, etc.) and how they fit into your workflow. Examples of projects where AI significantly accelerated your development process. Your availability over the next 2–3 months and your expected hourly rate.