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

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™

**ChatGPT Business + Google Workspace Integration Consultant** I'm looking for an expert to help set up ChatGPT Business as the central AI platform for my medical practice. I need assistance with: * Fixing/migrating my ChatGPT Business workspace * Connecting Gmail, Google Calendar, Google Drive, and Google Docs * Integrating Basecamp * Organizing Google Drive and workflows * Training me on best practices * Building AI workflows for meeting summaries, task management, and office operations This is an initial 3–8 hour project with the opportunity for an ongoing consulting relationship. **Requirements:** * Experience with ChatGPT Business * Google Workspace expertise * Basecamp experience (preferred) * AI workflow automation (Zapier, Make, n8n, etc.) * Excellent communication skills Please include: * Similar projects you've completed * Your hourly rate * Your availability * Why you're a good fit for this project

Overview I am seeking an experienced healthcare revenue cycle consultant who has extensive, real-world experience with both eClinicalWorks and athenahealth. Ideally, you currently work with one or both systems daily as a practice administrator, billing manager, consultant, implementation specialist, or revenue cycle leader. This is not a software implementation project. I am performing due diligence before selecting an EHR/Practice Management platform for a new multi-provider outpatient psychiatry practice. The goal is to understand the real-world strengths, weaknesses, hidden costs, workflow differences, automation capabilities, customer service, and billing implications of each platform from someone who has extensive practical experience. Required Qualifications 5+ years of experience with eClinicalWorks and/or athenahealth Extensive knowledge of: Medical billing Revenue cycle management Insurance claims Clearinghouses Behavioral health workflows Scheduling Reporting Financial operations Current or recent daily use of eClinicalWorks strongly preferred Psychiatry or behavioral health experience is a major plus Consultation Topics 1. Overall Recommendation Which system would you choose? Why? Which is better for outpatient psychiatry? Which is better for long-term growth? Which system has aged better? 2. Customer Service & Ongoing Support Customer service is one of the most important factors in our software decision. I am interested in your real-world experience after implementation, not the sales process. Overall Experience Overall customer service quality (1–10) Responsiveness Knowledge of support staff Ease of getting problems resolved Biggest frustrations Day-to-Day Support Support hours Phone support Live chat Customer Portal Email support After-hours support Emergency support Phone Support Do you generally reach a live representative immediately? Typical hold times Are calls routed overseas? Are most issues resolved during the first call? Typical callback times Customer Portal Typical response time Typical resolution time Ease of tracking tickets Escalation process Quality of follow-up Support Quality Knowledge of first-level support Frequency of escalation Typical turnaround for: "How do I..." questions Billing questions Reporting questions Technical issues Software bugs Account Management Dedicated account manager? Quarterly business reviews? Upgrade assistance? Proactive recommendations? Overall Comparison Compare customer support between eClinicalWorks and athenahealth. Which company provides the better long-term customer support experience? Which would you trust more for a growing practice? 3. Billing, Revenue Cycle Management (RCM) & Financial Reporting (Use the expanded RCM section we previously created, including revenue cycle workflow, contract fee schedules, allowables, reporting, analytics, automation, and overall comparison.) 4. Psychiatry Workflow Medication management Therapy Combined E/M + Psychotherapy billing 99213 99214 99215 90833 90834 90792 Documentation workflow Templates Intake workflow PHQ-9 / GAD-7 Behavioral health features 5. AI Functionality Sunoh AI Ambient listening AI note quality Time savings AI assistants Real-world usefulness Biggest limitations 6. Scheduling Scheduling workflow Waitlists Recurring appointments Open Access Self scheduling Appointment reminders Two-way texting Cancellation workflow Rescheduling workflow 7. Patient Communication Patient Portal Secure messaging Text reminders Two-way texting Intake forms Online check-in Balance reminders Telehealth notifications 8. Telehealth Ease of use Reliability Workflow Documentation integration Hidden costs 9. Hidden Costs Please identify any costs practices commonly overlook: Clearinghouse AI Telehealth Text messaging Patient statements Faxing Open Access Interfaces EPCS Wiley Practice Planners Additional modules Training Upgrades 10. Implementation Typical implementation Training Go-live Data migration Common mistakes 11. Phone System Integration & Call Center Workflow Please discuss how each platform integrates with modern VoIP phone systems. Supported Integrations RingCentral Nextiva Zoom Phone Dialpad GoTo Connect Vonage 8x8 Other recommended systems Workflow Can the software: Automatically identify patients using Caller ID? Pop the patient chart when a call arrives? Open today's appointment? Display upcoming appointments? Create tasks during the call? Document phone calls? Record calls (if desired)? Transfer calls internally? Click-to-call from within the chart? Click-to-text patients? AI & Automation AI call summaries Voicemail transcription Missed-call workflows AI assistants Appointment scheduling directly from incoming calls Reporting Call reports Missed calls Hold times Staff productivity Call recordings Quality assurance Recommendations Which phone system integrates best with eClinicalWorks? Which integrates best with athenahealth? Which would you recommend for a growing outpatient psychiatry practice? Are there any integrations that should be avoided? 12. Integrations Labs Pharmacies Clearinghouses Accounting software Payment processors Outlook / Microsoft 365 Other third-party integrations 13. Performance Speed Reliability Downtime Bugs Browser compatibility Mobile app 14. Favorite Features Top productivity improvements Most valuable features Hidden features Features most practices don't know about 15. Biggest Complaints Biggest frustrations Daily annoyances Workflow limitations Features that need improvement Deliverable I am looking for a 60–90 minute Zoom consultation with someone who has extensive practical experience using these systems—not a salesperson. Please include: Years of experience Current role Systems used Approximate number of practices supported Behavioral health experience Which system you would recommend and why Your hourly consulting rate Final Question If you were opening a brand-new outpatient psychiatry practice today with the goal of scaling from 1 provider to 20+ providers, which EHR, practice management system, AI solution, and phone system would you choose, and why? I am looking for an honest, unbiased assessment based on real-world experience. I am much more interested in practical workflow, customer support, operational efficiency, hidden limitations, and best practices than vendor marketing materials.

Senior Software Engineer (AI-Focused, Contract – US) Position Summary W Energy is seeking a Senior Software Engineer (Contract) to help drive the integration of AI capabilities into our core platform. This role is focused on building AI-powered product features, not just experimenting with models—embedding intelligence directly into workflows across our upstream and midstream solutions. You’ll design and implement AI-driven functionality that improves automation and user experience. This includes leveraging LLMs, machine learning models, and modern AI tooling within a production SaaS environment. This is a hands-on role for someone who can move quickly, make pragmatic decisions, and bring AI concepts into real, scalable product features. Responsibilities • Design and implement AI-powered features within the platform (e.g., automation, recommendations, copilots) • Integrate LLMs and/or ML models into existing services and workflows • Evaluate, select, and optimize AI tools, APIs, and frameworks for production use • Collaborate with Product to translate business problems into AI-driven solutions • Build and maintain scalable backend services to support AI functionality • Profile, test, and optimize performance of AI-integrated systems • Ensure reliability, security, and cost-efficiency of AI components in production • Contribute to architecture decisions around AI integration and system design • Partner with engineering teams to embed AI into existing applications without degrading stability Requirements • 5+ years of experience as a software engineer in a SaaS or cloud-based environment • Strong backend engineering experience (RoR and/or Golang preferred) • Experience integrating APIs and working within distributed systems • Hands-on experience with AI/ML tools (e.g., OpenAI, Anthropic, Hugging Face, or similar) • Experience building or integrating AI-powered features into applications (not just experimentation) • Strong understanding of data flow, system design, and performance optimization • Experience with relational databases (SQL Server or similar) • Familiarity with microservices architecture, Kubernetes, and CI/CD pipelines • Experience deploying applications in Azure or similar cloud environments • Strong problem-solving skills with ability to work in ambiguous, fast-moving environments • Builder mindset—someone who can take an idea and turn it into a working feature quickly • Pragmatic approach to AI (focus on value, not hype) • Ability to work independently in a contract environment while collaborating closely with internal teams • Strong communication skills and ability to explain AI concepts to non-technical stakeholders Preferred • Experience with prompt engineering, embeddings, or retrieval-augmented generation (RAG) • Exposure to model evaluation, fine-tuning, or AI performance monitoring • Experience with event-driven architectures or real-time data processing • Background in energy, fintech, or other complex data-driven industries

We are looking for an experienced AI trainer / speaker to deliver a 2–3 hour live, remote Introduction to AI training session for a B2B field sales team The audience will be group of sales professionals. The client is in the protective packaging and packaging automation industry. Their sales team works with customers on packaging materials, packaging processes, damage reduction, labor efficiency, sustainability, throughput, and automation-related opportunities. The goal of the session is to provide a practical and engaging introduction to AI usage in sales workflows. This should not be a highly technical AI course. The focus should be on helping sales professionals understand how AI can support their daily work and improve sales productivity. Desired session focus: Practical introduction to AI and generative AI for non-technical sales users How field sales teams can use AI safely and effectively AI for account research and customer meeting preparation AI for improving discovery questions and understanding customer pain points AI for writing better follow-up emails, summaries, and sales messaging AI for preparing customer-specific value propositions AI use cases relevant to B2B consultative sales Responsible AI use, including confidentiality, accuracy, and human review Live examples and practical demonstrations The ideal trainer should be able to make the session engaging, practical, and relevant to a sales audience. Experience training sales teams, B2B commercial teams, or business users on AI adoption is strongly preferred. Experience in manufacturing, packaging, industrial sales, logistics, automation, supply chain, or similar B2B industries would be a strong plus, but is not mandatory if the trainer can tailor examples appropriately. Trainer responsibilities: Prepare and deliver a 2 hour session Tailor examples to a B2B field sales audience Include practical AI demonstrations that sales professionals can relate to Explain AI concepts in simple business language Provide guidance on safe and responsible use of AI tools Keep the session interactive and engaging for the group Coordinate with us in advance to align the session with client goals Ideal candidate qualifications: Strong experience delivering AI, generative AI, or digital productivity training Comfortable presenting to business and sales audiences Ability to explain AI concepts without unnecessary technical complexity Strong communication and facilitation skills Experience with tools such as ChatGPT, Microsoft Copilot, Claude, Gemini, or similar AI platforms Ability to tailor training examples to client-specific business scenarios Prior experience with sales enablement, B2B sales workflows, or customer-facing teams is preferred Please include the following in your response: Brief summary of your AI training experience Examples of similar business or sales-focused AI sessions you have delivered Your approach for making a 2–3 hour AI session practical and engaging Any relevant industry experience with B2B sales, manufacturing, packaging, logistics, supply chain, or automation Your availability in August for this training session

About Us We are a forward-thinking AI enterprise software company building governance solutions. Our systems combine Python engineering, Natural Language Processing, and Machine Learning to deliver secure governance solutions. We’re seeking a Back-End Python Engineer with expertise in AWS deployed applications, GITHUB CI/CD pipelines, DJANGO, ML Pipelines, Endpoint Integration, Sagemaker, containerization, Use of AI to design front end applications and debug code. Key Responsibilities Design, develop, and maintain back-end services in Python to support software application Debug Application for Quality and Assurance Build Data Connectors for Application Integration Implement new features with front end design as needed Containerize and deploy services across AWS infrastructure. Build and scale RESTful APIs and microservices (Django + DRF) that integrate into automated pipelines. Tune system performance (network, I/O, memory, GPU utilization) for optimization. Architect and maintain databases (SQL & NoSQL), ensuring query optimization, high availability, and caching (Redis). Integrate background processing (Celery) and real-time communication (WebSockets) into containerized environments. Collaborate with DevOps, front-end, and AI/ML teams to deliver end-to-end automated workflows. Apply best practices in system design (SOLID, DRY, KISS), Python standards (PEP8), and secure infrastructure deployment. Qualifications Core Skills Proficiency in Python (OOP, async, functional programming, data structures). Expert-level knowledge of AWS Infrastructure (deployment, operators, CI/CD, scaling). Strong background in containerization (Docker, Podman) and Kubernetes-native orchestration patterns. Experience supporting AI Dev automation workflows and integrating back-end services with automated pipelines. Deep knowledge of Django & DRF: ORM, serializers, view sets, permissions, HTTP methods. Advanced database design & optimization for high-throughput applications. Familiarity with Redis caching, Celery task queues, and uWSGI/ASGI communication layers. Solid testing skills (pytest/unittest) and CI/CD pipelines with Git. Preferred Expertise Hands-on experience with GPU-enabled workloads and hardware acceleration in containerized environments. Familiarity with infrastructure automation tools (Ansible, Terraform, or similar). Agile/Scrum team experience and use of task tracking (Jira, Trello). What We’re Looking For We want an engineer who: PRIORITIZES SECURITY OF SYSTEMS AND INFRASTRUCTURE ACROSS SECURITY FRAMEWORKS Builds automation-first systems that support AI Dev workflows from code to deployment. Thinks about performance and scalability at the infrastructure + software level. Collaborates across teams (DevOps, AI/ML, product) to deliver fully integrated, automated platforms.

Overview We're a multi-division operating company (B2B transport + delivery operations) looking for a hands-on, AI-forward CPA to own our accounting and provide controller/CFO-level financial guidance. This is an ongoing part-time engagement (~10–20 hours/month) with room to grow. We want someone who uses modern tools and AI to work efficiently — not someone billing hours for manual data entry. You'll be the financial backbone of the business: keeping the books clean, the cash visible, and management informed enough to make good decisions. What you'll do Monthly close, bookkeeping oversight, and financial statement preparation (P&L, balance sheet, cash flow) Cash flow forecasting and weekly cash management across multiple bank accounts Vendor payment management and prioritization Tax planning, compliance support, and coordination with our tax preparer Sales tax issue management and resolution Financial analysis and forecasting across two operating divisions Shareholder/investor reporting support Due diligence and transaction support as needed Strategic financial guidance, including during periods of financial pressure or restructuring Must-haves Active CPA license Demonstrated use of AI in your workflow (e.g., automating categorization/reconciliation, document extraction, forecasting models, reporting). Tell us specifically how you use it. Strong QuickBooks Online experience Multi-entity / multi-division accounting experience Cash flow forecasting and management experience Comfortable advising owners directly and communicating clearly with non-finance stakeholders Discreet and reliable with sensitive financial information Nice-to-haves Experience with companies that have navigated tight cash periods, restructuring, or turnaround Sales tax / multi-state compliance experience Experience supporting fundraising, investor reporting, or M&A/diligence Industry experience in logistics, delivery, transport, or regulated/cash-intensive businesses How to apply In your proposal, please include: Your CPA license status and state. A specific example of how you use AI tools in your accounting/finance workflow and the time it saves. A brief example of a cash flow or restructuring situation you helped a client navigate. Your typical availability and turnaround time. Your hourly rate (and any monthly retainer option).

Summary: We are building Sphere Inc., an AI-powered SaaS platform focused on the real estate industry. The product is currently in the MVP stage, and we are looking for a strong full-stack developer who can help us build, refine, and launch the first working version. The platform is designed to help real estate businesses automate daily operations, improve decision-making, and use AI agents to support workflows such as property management, lead handling, deal analysis, document processing, reporting, and business automation. This is not a basic website project. We are building a real SaaS product with a modern frontend, reliable backend, AI-powered workflows, and scalable AWS infrastructure. Current Project Status: The product vision and core direction are already defined. We are currently shaping the MVP workflows, user experience, and technical structure. At this stage, our main need is execution. We need someone who can help turn the concept into a working MVP that can be tested with real users. Some workflows are still being refined, so we are looking for a developer who can contribute both technically and practically — not just write code from fixed tickets. Current Progress & Bottlenecks We have a clear direction, but need support with - Structuring the MVP architecture - Building the frontend and backend features - Designing practical AI agent workflows - Connecting AI features with real estate data and user actions - Setting up AWS infrastructure for development and deployment - Creating a clean experience for non-technical business users - Prioritizing the most important MVP features The main bottleneck right now is moving from concept/prototype stage into a stable, usable product. We are looking for someone who has experience with - Building SaaS products from MVP to production - Full-stack development with Python, Node.js, JavaScript, and TypeScript - Frontend development using React, Next.js, or similar frameworks - Backend APIs, database design, authentication, and user roles - AI agents, LLM integrations, workflow automation, or RAG - AWS deployment, storage, monitoring, and security - Real estate platforms, CRMs, property data, document workflows, reporting, or automation tools - Writing clean, maintainable, and scalable code Tech Stack: Python, Node.js, JavaScript/TypeScript, React or Next.js, PostgreSQL, AWS, REST APIs, and AI/LLM tools such as OpenAI, Anthropic, AWS Bedrock, LangChain, LangGraph, or similar frameworks. Responsibilities: - Build frontend and backend features for the MVP - Design and implement AI-powered workflows and agent features - Connect APIs, databases, authentication, and user roles - Set up or improve AWS infrastructure - Help prioritize features and identify technical risks - Communicate progress clearly and regularly Some Knowledge That Is a Plus: - Real estate CRM, property management, brokerage, acquisitions, or leasing platform experience - AWS Bedrock, LangChain, LangGraph, or vector databases - Multi-tenant SaaS architecture - Stripe or subscription billing - DevOps, Docker, CI/CD, and testing - Analytics dashboards, reporting tools, or document automation The ideal candidate is a reliable full-stack developer who can work independently, understand the product vision, ask smart questions, and help us make practical technical decisions during the MVP stage. We need someone who is comfortable in an early-stage environment and can help turn a clear idea into a working SaaS product for real estate users. Please include: 1. Your GitHub, portfolio, or examples of previous work 2. A brief description of your related SaaS experience 3. Examples of AI agent, AI automation, or LLM-powered products you have built 4. Your AWS experience 5. Any real estate platform, CRM, data, or automation experience 6. Your availability and preferred working style We are looking for someone who can help us build the MVP now and potentially continue with us as the platform grows.

Build scalable technical capabilities that support governance automation, data readiness, and AI Business Use Cases enablement Implement metadata-driven workflows, Databricks integrations, quality/scorecard pipelines, access patterns, and reusable platform services Partner with governance data engineering, security and UX teams to ensure solutions are secure, maintainable, automated and repeatable across BU’s