Overview We’re looking for an experienced AI engineer or AI systems builder to help us design and build an internal intelligence layer that turns fragmented customer data into actionable growth opportunities. Right now, customer insights live across multiple disconnected systems — CRM notes, product usage data, emails, support tickets, and spreadsheets. While the data exists, it is not structured in a way that helps us proactively identify expansion opportunities, churn risks, or account-level next steps. We want to build an AI-driven system that continuously synthesizes this information and helps our team understand: * What is happening inside each account * Where expansion or upsell opportunities exist * Which accounts are at risk and why * What the next best action should be for each customer ⸻ What You’ll Build You will design and implement an AI system that can: * Ingest structured and unstructured data (CRM, emails, notes, product signals) * Build dynamic “account intelligence profiles” for each customer * Identify patterns across accounts (usage drops, feature gaps, expansion signals) * Generate clear, human-readable account summaries * Recommend next-best-actions for sales, customer success, or leadership * Surface expansion opportunities based on behavioral and contextual signals * Flag risk signals early with supporting reasoning ⸻ Ideal Output For each account, the system should be able to generate: * A concise account narrative (“what’s going on here”) * Key signals and anomalies * Expansion opportunities (with rationale) * Risk factors (churn or stagnation indicators) * Suggested actions for the team this week * Confidence level and supporting evidence ⸻ Why This Matters We are sitting on a large amount of customer data, but most of it is passive. The goal is to turn it into an active intelligence system that helps our team: * Prioritize the right accounts * Increase expansion revenue * Reduce churn risk * Spend time on the highest-impact opportunities This becomes a core internal system that directly impacts revenue efficiency and customer outcomes. ⸻ Ideal Candidate We’re looking for someone with experience in: * LLM-based systems and agentic workflows * Data pipelines and multi-source data ingestion * Prompt engineering + structured reasoning systems * CRM systems (Salesforce, HubSpot, etc.) * Customer analytics / product analytics * Building internal AI tools or copilots * Backend + API integration work Bonus if you’ve worked on: * RevOps tooling * Customer success platforms * Data enrichment or account intelligence systems * SaaS growth analytics ⸻ Deliverables * System architecture for AI customer intelligence layer * Data ingestion and normalization approach * Prompting / reasoning framework for account analysis * Prototype system (or working MVP) * Output format for account intelligence reports * Documentation for internal expansion and scaling * Recommendations for tooling (build vs buy decisions) ⸻ Engagement This starts as a project-based build, but could expand into a long-term role as we scale the system across our entire customer base and additional workflows. ⸻ To Apply Please include: * Examples of AI systems or agentic workflows you’ve built * Experience integrating LLMs with real business data * Your recommended architecture for a system like this * Any clarifying questions you’d want answered before starting

We are seeking a skilled freelancer to build a directory for artificial intelligence agents designed for financial advisors. The project involves creating a comprehensive directory that highlights AI solutions for financial advisors, including their features, pricing, and reviews. The ideal candidate will have experience in AI and directory building, with a strong understanding of the financial services industry.

## Project Overview I am seeking an experienced Senior AI Systems Architect / Full-Stack Engineer to evaluate and potentially lead the technical architecture of a new enterprise software platform currently under development. At this stage, I am not looking for someone to simply write code. I am looking for an experienced technical professional capable of evaluating architecture, recommending technologies, and helping define the engineering roadmap for Version 1. The project involves the integration of artificial intelligence, enterprise software architecture, workflow automation, secure data management, API integrations, and cloud-based application design. Because the project contains proprietary intellectual property, detailed information will not be disclosed during the initial interview process. Candidates selected to move forward will be asked to execute a Non-Disclosure Agreement before reviewing project documentation. ## Initial Objectives • Review the existing project at a high level. • Evaluate technical feasibility. • Recommend the most appropriate technology stack. • Design the production architecture. • Develop an implementation roadmap. • If mutually agreed, continue as the lead software architect for Version 1. ## Required Experience Applicants should have significant experience with: • Enterprise software architecture • Artificial Intelligence integration • API development and integration • Full-stack application development • Cloud architecture and deployment • Database design • Authentication and application security Excellent communication skills are important. I am looking for someone who enjoys solving complex architectural challenges and who is interested in building something from the ground up. ## Please Include 1. A brief summary of your architecture experience. 2. Examples of enterprise software systems you have helped design. 3. AI-related experience. 4. Your preferred technology stack. 5. Why this opportunity interests you. The initial engagement is intended as an architectural evaluation. A longer-term relationship may develop if there is a strong mutual fit.

I want to build a private multi-model RAG-based Opportunity Intelligence Agent. It should support document ingestion, opportunity-specific workspaces, vector search, source citations, multi-model routing across OpenAI, Claude, Perplexity, and possibly DeepSeek, and generate strategic recommendations from both uploaded files and live web research. This is intended to become a reusable base agent capable of knowledge retrieval, web research, multi-model orchestration, document analysis, citation generation, and agent clonding and configuration. It will be used for analyzing & strategy development for project opportunities, responding to RFPs, and proposal assistance, as well as other applications.

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 building a next-generation workflow automation platform that combines deterministic business rules, artificial intelligence, document intelligence, and human review workflows into a single operating system. This is not a traditional CRM project. Our vision is to develop a doctrine-driven platform where business rules serve as the system authority, AI serves as an analytical and drafting layer, and human reviewers serve as the final compliance checkpoint. We are seeking an experienced engineer or engineering partner who can help architect and build the platform from the ground up. Project Objectives The platform will: • Ingest and analyze large volumes of structured and unstructured documents • Extract data from reports, PDFs, and supporting documentation • Apply rule-based workflow logic • Generate AI-assisted recommendations and draft outputs • Maintain complete audit trails and workflow transparency • Route work through human review checkpoints • Support future deployment of local AI infrastructure for privacy and performance Core Architecture The system will be built around four primary layers: 1. Rules Engine * Deterministic business logic * Workflow orchestration * State management * Trigger and escalation logic * Audit tracking 2. AI Layer * Document analysis * Classification * Pattern detection * Summarization * Draft generation * Structured outputs 3. Local Processing Layer * OCR * Document parsing * Data extraction * Vector search * Local inference capabilities * Privacy-first processing 4. Human Review Layer * Quality assurance * Workflow approvals * Compliance review * Exception handling Initial Development Priorities Phase 1 • User authentication • Client record management • Document upload system • OCR and document extraction • Workflow engine • Rule-based status management • Review dashboard Phase 2 • AI-powered document analysis • Automated classification • Recommendation engine • Draft generation workflows • Response parsing Phase 3 • Local AI infrastructure • Vector database integration • Knowledge retrieval system • Multi-agent workflow orchestration • Advanced automation Desired Technical Experience Required • React / Next.js • Node.js, Python, or similar backend framework • PostgreSQL or equivalent relational database • REST APIs • Cloud infrastructure (AWS, Azure, or GCP) • Workflow automation systems • Document processing pipelines Preferred • OpenAI APIs • Anthropic APIs • Retrieval-Augmented Generation (RAG) • LangGraph, LangChain, or similar frameworks • Vector databases • OCR technologies • AI agent architectures • NVIDIA AI ecosystem • Local model deployment What We Are Looking For We are not looking for someone who simply builds forms and dashboards. We are looking for a builder who understands how to combine: • Rules engines • Artificial intelligence • Workflow automation • Human review systems • Scalable software architecture The ideal candidate enjoys solving complex business process problems and translating expert decision-making into software systems. Engagement Structure Open to: • Fractional CTO • Lead Architect • Senior Full-Stack Engineer • AI Systems Engineer • Development Agency • Long-term strategic technology partner To Apply Please provide: • Relevant project examples • Experience building workflow automation platforms • Experience with AI-powered applications • Technology stack recommendations • Estimated availability • Preferred engagement structure

Overview We are a growing, privately held group of operating companies in the heavy equipment, equipment dealership, auction, rental, service, parts, and logistics industries. We are seeking an experienced Fractional Chief Information Officer to assess our current technology environment, develop a practical technology roadmap, and help lead the implementation of priority initiatives across multiple business units and locations. We are looking for a business-oriented technology leader who can help us improve: Systems integration Process automation and AI implementation Data and Inventory visibility CRM adoption and accountability Executive dashboards Phone system capabilities Cybersecurity oversight Vendor accountability Standardization across operating companies and locations The ideal consultant will be comfortable moving between strategy and execution. We do not need a report that sits on a shelf with no one to execute it. We need a leader who can identify priorities, simplify decisions, select the right vendors and tools, and help drive implementation. Business problem to solve Our businesses have grown across multiple operating models, systems, vendors, and locations. We need stronger visibility, more consistent processes, and better integration between the systems that support sales, inventory, rentals, service, parts, auctions, logistics, marketing, finance, and leadership reporting. We want to reduce duplicate data entry, improve the quality and timeliness of information, strengthen accountability, and give leadership trusted data for faster decision-making. Initial engagement We expect the initial engagement to include a structured technology and business systems assessment, followed by a prioritized roadmap. Phase 1 deliverables: Assess the current technology environment, including key systems, vendors, integrations, data flows, reporting processes, and operational pain points. Identify urgent risks, quick wins, and longer-term priorities. Develop a practical 12- to 24-month technology roadmap with sequencing, estimated resource needs, decision points, and recommended ownership. Evaluate our current phone system environment and recommend a plan for upgrade, vendor selection, and implementation. Recommend an approach to improve inventory visibility, including equipment locations, attachments, transfers, rental status, and related reporting needs. Develop a business intelligence and dashboard strategy for leadership reporting. Evaluate CRM adoption, data quality, workflow consistency, and sales management visibility. Identify practical AI and automation opportunities that can save time, improve reporting, strengthen customer response, and reduce repetitive manual work. Review cybersecurity posture, vendor coverage, disaster recovery, business continuity, and major risk gaps. Recommend an implementation governance model, including decision rights, project cadence, vendor accountability, and progress reporting. Likely implementation priorities The exact sequence will be finalized after the initial assessment, but current priorities include: Immediate priorities Technology assessment and systems inventory Phone system upgrade and implementation planning Practical AI strategy and initial use cases Inventory visibility improvements Data ownership and reporting standards Near-term priorities Executive dashboard development CRM optimization and adoption Integration between CRM, accounting, inventory, rental, dealership, auction, logistics, and marketing systems Reduction of duplicate data entry and spreadsheet-based reporting Vendor performance management Cybersecurity and continuity improvements Longer-term fractional CIO responsibilities Depending on fit and the assessment results, the selected consultant may continue in an ongoing fractional capacity to: Lead technology roadmap execution Oversee system selection and implementation projects Coordinate internal stakeholders and outside vendors Improve data governance and reporting reliability Develop executive dashboards and KPI visibility Support CRM adoption and process standardization Identify and implement workflow automation Guide responsible AI adoption Strengthen cybersecurity oversight Establish repeatable technology decision-making standards Provide executive-level recommendations on technology investments What this role is not This is not primarily a role for: Routine help desk management Printer, laptop, or desktop troubleshooting Server administration Basic managed IT support A pure software developer A consultant who only produces recommendations without implementation support We need someone who can translate business problems into practical technology solutions and help drive adoption across the organization. Required experience Candidates should have meaningful experience leading business systems improvement in a multi-location or multi-business operating environment. Strong candidates will have experience with several of the following: Fractional CIO, CIO, CTO, VP of Technology, enterprise applications, or technology transformation leadership Systems integration and enterprise architecture ERP, accounting, CRM, inventory, rental, or operational systems Business intelligence tools such as Power BI or Tableau Dashboard development and executive KPI reporting Data governance and reporting standardization Vendor selection, contract management, and implementation oversight AI use-case identification and workflow automation Cybersecurity oversight, disaster recovery, and business continuity Change management and user adoption Privately held, family-owned, founder-led, or entrepreneurial businesses Experience in equipment dealerships, distribution, rental businesses, logistics, construction, agriculture, automotive dealerships, industrial services, or other multi-location operational environments is strongly preferred but not required. Working style The right person will be: Business-oriented Practical and execution-focused Comfortable challenging unclear priorities Able to simplify complex decisions Comfortable working with ownership, executives, department leaders, branch teams, and outside vendors Willing to get into the details without losing strategic perspective Focused on measurable business outcomes rather than technology for technology’s sake Some onsite discovery work and periodic travel to company locations may be required. Please indicate your availability for onsite work in Pennsylvania and Maryland. What success looks like Success should produce measurable improvement in the business, including: Leadership has reliable visibility into key business metrics Reporting becomes faster, more automated, and more trusted Inventory locations and status are easier to understand CRM adoption and sales visibility improve Systems communicate more effectively Duplicate data entry and manual reporting are reduced Technology vendors are held accountable AI and automation create measurable productivity gains Cybersecurity risks are better understood and addressed Ownership spends less time searching for information and more time making decisions Please include the following in your proposal A brief description of two or three similar engagements you have led. An example of a multi-location, multi-business, dealership, distribution, rental, logistics, or operational environment you have supported. Your approach to the first 90 days of an engagement like this. Your experience with ERP, CRM, inventory, rental, accounting, phone, reporting, and business intelligence systems. Your experience implementing AI or workflow automation in a practical business setting. Your approach to vendor selection and vendor accountability. Your preferred engagement model, expected weekly availability, hourly or project rate, and ability to support onsite discovery work. Whether you personally lead the work or delegate significant portions of the engagement to other team members.

I am building Dewy, an early-stage construction technology platform focused on construction buyout and subcontractor quote intelligence. The first MVP is intentionally narrow: users should be able to upload subcontractor quote/proposal documents and receive structured outputs showing included scope, exclusions, assumptions, qualifications, cost structure, alternates, allowances, and potential risk flags. I have already developed the product concept, construction logic, early workflows, and prototype direction using Codex/AI tools. I am not looking for someone to invent the product from scratch. I need a senior AI product engineer who can review what I have, determine what is usable, define a clean MVP architecture, and help turn the current direction into a working private beta. Initial scope: * Review the current prototype/code/product materials. * Identify what should be reused vs. rebuilt. * Recommend the MVP architecture and tech stack. * Define the AI document-processing workflow. * Design the structure for file upload, extraction, editable results, and export. * Help create a realistic build roadmap, timeline, and budget. * Potentially continue into hands-on MVP development if there is a strong fit. Ideal experience: * Full-stack SaaS / MVP development * AI / LLM application development * OpenAI API or similar model integrations * Document extraction or document intelligence workflows * PDF/DOCX parsing and structured data extraction * React / Next.js * Python * APIs and backend workflows * Supabase/Postgres or similar database experience * Vercel or similar deployment experience * Ability to work with a non-technical founder and translate business goals into a practical build plan This is not a full enterprise platform build yet. The first MVP should stay focused on one core workflow: Subcontractor quote documents in → structured buyout intelligence out. Please respond with: 1. Relevant AI/document extraction projects you have built. 2. How you would approach the MVP architecture. 3. Whether you recommend starting with an audit/roadmap before build. 4. Your hourly rate and availability. 5. Whether you are interested in ongoing build involvement after the initial review.

We’re looking for experienced AI professionals to provide short, original quotes, practical insights, and light content feedback for our educational articles and guides. Your real-world perspective will help make the content more accurate, useful, and trustworthy for readers. The initial project involves reviewing and contributing to one guide, with the possibility of ongoing work. Example guide: onlinemastersdegrees.org/best-programs/information-systems/ **What You’ll Do:** * Review AI education content for accuracy and clarity * Leave light feedback through Google Docs comments * Provide brief expert quotes, usually 2–5 sentences each * Offer practical insights based on real-world AI, machine learning, or data science experience * Help add context around AI careers, degree programs, certifications, skills, tools, and industry expectations **For the Initial Project:** We’re looking to add approximately 3–4 short expert quotes to one AI guide. Quotes should be original, practical, and based on your professional experience. **Details:** * $30 per page * Pages typically take 20–30 minutes * Clear guidelines and examples provided * Contract, flexible, and ongoing work **Relevant Experience May Include:** * Artificial intelligence * Machine learning * Data science * Generative AI * Natural language processing * Computer vision * AI product development * MLOps * AI governance, risk, or compliance * Responsible AI * AI education or workforce development **In your submission, please include:** 1. A few sentences about your AI background, professional experience, and areas of expertise 2. Any relevant degrees, certifications, credentials, or notable AI projects 3. Link to your LinkedIn profile To help us sort through automated submissions, please put the name of Shopify’s CEO at the top of your submission.

AI Engineer (RAG & Agentic Workflows). *LLM RESPONSES AUTOMATICALLY AVOIDED* We have already launched a production generative AI product that utilizes a custom Retrieval-Augmented Generation (RAG) architecture. We are now expanding the platform to include CRM intelligence, workflow automation, and agentic AI capabilities. This is **not** a prompt engineering role. Seeking an engineer with deep experience building and deploying production AI systems that combine LLMs with multiple structured and unstructured data sources. You should be comfortable walking into an existing, complex codebase, understanding the current architecture, and improving it. Existing AI Architecture Our current AI architecture consists of: * OpenAI embeddings * Embeddings stored in MongoDB * MongoDB Atlas Vector Search for retrieval * Retrieval from both structured SQL data and unstructured document collections * Existing tool/function-calling architecture **Please do not apply if you have not previously built or maintained production RAG systems using embeddings and vector search.** Experience specifically with **OpenAI embeddings and MongoDB Atlas Vector Search** is highly preferred. CRM Intelligence Layer We are currently building a CRM platform and need the AI to reason over CRM records, including the other records are RAG currently retrieves. You will be responsible for designing and implementing the AI integration layer that enables the LLM to intelligently retrieve and reason over CRM data. This work includes: * Designing AI tools/functions that expose CRM data to the LLM. * Implementing backend tool handlers that retrieve CRM records. * Defining tool schemas and instructions so the AI knows when and how to retrieve CRM information. * Building secure retrieval mechanisms that enforce strict user and organization-level access controls. * Transforming raw CRM records into structured, AI-ready context. The AI will need to reason across: * CRM contacts and organizations * client profiles * Deals and opportunities * Projects * Tasks and reminders * Notes * Email history * SMS and WhatsApp communications * Call transcripts * Meeting summaries * Documents and contracts * Workflow history Agentic AI & Workflow Automation * Build proactive AI agents that generate alerts, recommendations, follow-ups, reports, and suggested next actions. * Design systems capable of reasoning across both structured and unstructured data sources. * Architect and implement multi-step and multi-agent workflows. * Develop workflow intelligence that assists users in completing real-world business tasks. Required Experience * Demonstrated experience building and deploying production AI systems used by real customers. * Experience working with embeddings, vector databases, and retrieval pipelines. * Experience implementing LLM tool/function-calling architectures. * Experience integrating AI systems with business systems such as CRMs, ERPs, or other operational databases. * Experience combining structured and unstructured data within AI applications. * Strong backend engineering and systems architecture experience. * Demonstrated ability to quickly understand and improve existing codebases. * Ability to independently own and deliver complex technical initiatives. Strongly Preferred * Experience with OpenAI embeddings. * Experience with MongoDB Atlas Vector Search. * Experience building agentic AI systems and workflow automation. * Experience designing long-term memory architectures. * Experience building multi-tenant SaaS applications with strict authorization requirements. * Experience implementing evaluation and monitoring pipelines for production AI systems. What We Value * High accountability and ownership. * Strong communication skills. * Product thinking and user empathy. * Ability to understand user workflows before writing code. * Pragmatism and sound engineering judgment. PLEASE DO NOT WASTE OUR TIME IF YOU NOT MEET THE REQUIREMENTS