We are seeking a skilled GenAI engineer to work with our client in a remote or Chicago-based capacity. The ideal candidate will have experience in developing and implementing AI solutions, with a strong understanding of machine learning and data analysis. Responsibilities include designing AI models, integrating AI into existing systems, and collaborating with cross-functional teams to enhance AI capabilities. If you have a passion for AI and a proven track record in delivering innovative solutions, we would love to hear from you.

## 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.

We’re a strategic investor relations firm looking to bring on an AI automation specialist to help us build and maintain internal workflows across key deliverables. This is not a one-off project. We’re looking for someone who can work with us ongoing, think in systems, and help us implement a structured AI-driven operating model. We already have a clear roadmap and initial strategy in place. Your role is to help bring it to life. What We’re Building We are focused on three core workflows: 1. AI-Assisted Drafting (Priority) Build Custom GPTs for client deliverables (earnings materials, summaries, etc.) Structure inputs (transcripts, historical materials, models) Standardize outputs (tone, format, consistency) 2. Analyst Consensus Automation Pull data from Excel / SharePoint (NOT third-party APIs) Use Make.com (or similar) to automate workflows Feed structured data into ChatGPT for report generation 3. Fund Profile Workflows (Later Phase) Work with semi-manual datasets (exports from IR platforms) Build hybrid workflows (automation + human input) What We Need From You We are looking for someone who can: Build Custom GPT workflows (not just prompts) Work with Make.com (or similar tools like n8n / Zapier) Integrate with Microsoft 365 (SharePoint, Excel, Outlook) Structure messy workflows into repeatable systems Think critically about data accuracy and validation Build with human-in-the-loop review processes Important Context We work in investor relations → accuracy matters more than speed We are not looking for “full automation” → we want controlled, reliable workflows Many processes today involve manual copy/paste and disconnected tools Goal is to reduce friction and standardize output, not cut corners Engagement Details Type: Ongoing (starting with a pilot project) Initial Focus: Build Phase 1 (AI drafting workflow) Hours: Flexible (will grow over time) Start: ASAP How We’ll Evaluate Candidates We’re less concerned with resumes and more interested in how you think. Please include: Examples of automation workflows you’ve built (especially with GPT + Make/Zapier) A short explanation of how you would: Take a transcript + prior materials → generate a structured draft Tools you prefer and why Nice to Have (Not Required) Experience in finance, IR, or data-heavy environments Experience working with structured Excel models API experience What This Can Turn Into If this goes well, this becomes a long-term role helping us build and maintain our internal AI infrastructure. We’re not looking for a task-doer—we’re looking for someone who can grow with us and own this function over time.

Create an A.i sales agent that automates tasks in real estate

We are seeking an AI Automation Implementation Expert to enhance our business processes. The ideal candidate will have experience in implementing AI solutions to improve efficiency and productivity. Responsibilities include analyzing current systems, identifying areas for automation, and implementing AI-driven solutions. The candidate should be able to work independently and provide insights on AI best practices.

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

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

We're building a confidential, AI-native operating system for high-volume plaintiff-side litigation. This is not a generic legal chatbot. It's an operating system for litigation operations — and we already have a live law firm as the proving ground, a working visual prototype, a pitch deck, and a near-term demo deadline. We need a senior full-stack engineer who can take an existing prototype, schemas, prompts, and workflow package and turn it into a secure working demo, then a production-track MVP. The right person thinks like a product architect, engineer, and security operator at once — fast, but disciplined with confidential legal data. Required: React/Next.js, TypeScript, Node or Python/FastAPI, PostgreSQL, auth and role-based access control, OpenAI or comparable LLM APIs, structured JSON/schema outputs, secure file handling, PDF/export generation, GitHub workflows, and strong security discipline. Strong plus: Legaltech, plaintiff-side litigation, case management systems (Filevine, Litify, Clio, Salesforce, HighLevel), RAG/document extraction, audit logging, and SOC 2 / PII / regulated-data experience. Ground rules: NDA required. No public repos. No real client data in the demo — sanitized data only. No API keys in browser code. No external sharing or deployment without approval. First deliverable: A build-readiness report identifying what's mock, what's reusable, and what needs rebuilding, plus architecture, security risks, database plan, API integration path, and a 7–30 day build roadmap. The path: Paid 7-day build-readiness sprint → 30-day demo sprint → longer-term technical lead / founding engineer discussion. To apply, please include: A short note on why you're right for this project 2–3 relevant products you've built (links) GitHub or code samples, if available Your availability for a 7-day build-readiness sprint Your hourly rate, fixed sprint price, or contract-to-hire preference Remote acceptable. U.S.-based preferred; South Florida a plus.

**** Agencies are welcome to apply, but put your GenAI solutions architect on the very first interview call. Not a salesperson who then hands off **** We are an AWS partner company focussed solely on Data & AI implementation work. We are specifically looking for a GenAI Solutions Architect to lead pre-sales engagements with customers. Typical duties include: 1/ Executing pre-sales discovery calls with customers 2/ Demoing our proprietary production GenAI demo environments and mapping customer use-cases 3/ Performing deep GenAI assessments and helping provide roadmap for implementation and planning 4/ Compile Statement of Work for customer projects We have a number of tools that will assist in this process to ensure compliance with our patterns and standards. The ideal candidate should have at least 3 yrs of hands on GenAI implementations, understanding of RAG, MCP, bedrock, agentcore experience, strands/langchain experience, and the ability to clearly understand business needs and articulate/map out to technical implementations. Our interview process is unique since this is a LONG running engagement: Step 1: Interview with a technical executive Step 2: Interview with a Sr. technical engineer Step 3: After NDA is signed, train on one of our IP environment (5 hr commitment from your side, but likely faster if you are already familiar with GenAI & AWS) Step 4: Present to a panel on the concept. This is where we evaluate your ability to present what you have learned and earn trust, similar to how you would be doing customer facing.

Looking for general help building out a platform for an AI saas.