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

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 

Overview We're building an open-source CLI gateway for multi-agent AI orchestration — model-agnostic, MCP-native, and designed to bring any agent framework online with a single command. The repo is active, well-documented, and growing. We need an engineer to accelerate integration coverage and help attract open-source contributors. The Work Build agent templates and runnable examples for LangGraph, CrewAI, and similar frameworks Add LLM provider support (Groq, Mistral, Gemini, etc.) to the Hermes runtime Write clean, contributor-friendly code that models good PR hygiene Submit work via fork → PR → merge workflow on GitHub You Are Strong Python developer with CLI tooling experience Familiar with at least one of: LangGraph, CrewAI, LiteLLM, LangChain Comfortable with open source GitHub workflows (fork, PR, issues, reviews) Self-directed — you read docs, ask good questions, and don't wait to be unblocked Nice to Have Experience with MCP (Model Context Protocol) Familiarity with SSE, OAuth 2.1, or agent credential management Prior open source contributions Engagement Part-time to start, 20 hrs/week Fixed milestones per integration delivered Potential to grow with the project To Apply Share your GitHub profile and one example of open source work or a project that shows your Python and agent framework experience. https://github.com/ax-platform/ax-gateway

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

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)

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

Company Overview Pay Ready is a leading provider of innovative payment solutions tailored for the property management industry. We help property owners and managers streamline financial processes and accounts receivable functions, including processing current and post-resident rent payments and recoveries. As we integrate Generative AI (GenAI) across our operations, we're seeking a Senior Software Developer to drive the development of AI-powered solutions that enhance both internal workflows and customer-facing applications. Position Overview Joining our team as a Senior Software Developer – Generative AI means being at the forefront of innovation, working on cutting-edge projects that are shaping the future of AI and machine learning. You'll have the opportunity to collaborate with top experts in the field, contributing to groundbreaking research and development that has real-world impact. We offer a dynamic and collaborative work environment where your ideas and contributions are valued, and where you'll have the resources and support needed to bring your vision to life. Being part of our team means embracing a culture that fosters continuous learning and professional growth, with access to ongoing training and development opportunities. You'll work on diverse and challenging projects, gaining valuable experience and expertise that will set you apart in your career. Key Responsibilities - Design and develop AI-driven applications that address both internal operational needs and external client requirements. - Utilize frameworks such as LangGraph and LangSmith to build, orchestrate, and monitor AI workflows. - Implement solutions that integrate seamlessly with existing systems, ensuring reliability and scalability. - Work in tandem with project managers and product owners to understand project scopes, timelines, and deliverables. - Participate in sprint planning, code reviews, and team meetings to ensure alignment and timely delivery of projects. - Provide technical insights and recommendations during the planning and execution phases. - Develop and refine AI models, ensuring they meet performance and accuracy benchmarks. - Monitor and analyze AI application performance, making necessary adjustments to optimize outcomes. -Stay updated with the latest advancements in AI and machine learning to incorporate best practices into development processes.

I need an expert senior software engineer that can provide consulting services around implementation best practices of LLM's and AI into existing application workflows. i.e. leveraging AI to extract data from a document as part of an ingestion pipeline.

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

I need help completing this project today. The project sits between data engineering and AI. I have source data that needs to be cleaned, structured, and prepared so it can support both analytics and RAG-style AI workflows. The goal is to create a reliable pipeline that takes raw data, normalizes it, preserves basic metadata/lineage, and outputs clean datasets that can be used for dashboards, vector indexing, or internal AI tools. The work may include: - Reviewing the current source data and structure - Cleaning and normalizing datasets - Designing or improving an ETL/ELT flow - Preparing AI-ready data for RAG or vector search - Adding basic validation checks - Organizing outputs for analytics use - Documenting the final workflow clearly Ideal freelancer has experience with: - Python and SQL - Data engineering / ETL pipelines - Databricks, Spark, or similar tools - RAG data preparation - Data cleaning, validation, and modeling - Cloud data storage such as S3, Postgres, or similar This is urgent and must be completed today. Please only apply if you are available immediately and can work quickly without a lot of hand-holding. When applying, please include: - Your relevant experience with AI-ready data pipelines or RAG data preparation - The tools you would use - Confirmation that you can complete this today - Your estimated timeline for delivery