We need a developer to build a simple AI chatbot MVP using Next.js and the OpenAI API. The chatbot should allow a business owner to enter FAQ or support content, then let users ask questions through a chat interface. The AI should answer based only on the provided content.

I need a full-stack developer to build a simple web application for an academic research study. Participants will access the application from a Qualtrics survey, have a voice conversation with an AI assistant using the OpenAI API, and then automatically return to Qualtrics to complete questionnaires. The application should support two experimental AI conditions, capture conversation transcripts, record participant metadata (ID, timestamps, condition, duration), and securely store the data for export. This is a research interface—not a commercial chatbot—and the focus is on reliability, security, and clean integration with the OpenAI API. Experience with React, Node.js, and OpenAI API integrations is preferred.

### Job Description: AI Chatbot Developer We are excited to announce an opening for an experienced and innovative developer to join our dynamic team in the pursuit of creating an advanced AI Chatbot. This chatbot will be designed to perform essential business functions, including but not limited to lead generation, quoting, and providing exceptional customer support. Our ideal candidate will possess a robust background in AI technologies, particularly in the realm of chatbot development, and will be equipped with outstanding problem-solving skills that enable them to tackle complex challenges with creativity and efficiency. As a key member of our development team, you will collaborate closely with various departments to gain a comprehensive understanding of our specific operational needs and requirements. Your ability to translate these needs into a functional and user-friendly chatbot solution will be critical to enhancing our overall operational efficiency. We are looking for someone who is not just technically proficient but also possesses a keen sense of business acumen to ensure that the chatbot aligns with our strategic goals. In this role, you will be responsible for various aspects of the chatbot development lifecycle, including but not limited to: - Designing and developing the conversation flow and user interface of the chatbot, ensuring it is intuitive and engaging for users. - Implementing natural language processing (NLP) capabilities to enable the chatbot to understand and respond to user inquiries accurately. - Integrating the chatbot with existing systems and databases to facilitate seamless access to information necessary for lead generation, quoting, and customer support functions. - Conducting rigorous testing and quality assurance to ensure the chatbot performs reliably and meets user expectations. - Analyzing user interactions and feedback to continuously improve the chatbot's performance and expand its capabilities over time. - Staying current with the latest advancements in AI technologies and chatbot development to incorporate best practices and innovative solutions. You will also play a crucial role in training team members on how to utilize the chatbot effectively and will be expected to provide ongoing support and maintenance to ensure the chatbot remains up-to-date and functional. If you have a passion for artificial intelligence, a deep understanding of customer engagement strategies, and a desire to make a significant impact within our organization, we would love to hear from you! Join us in revolutionizing the way we interact with our customers and streamline our business processes through cutting-edge technology. This is a fantastic opportunity for someone looking to advance their career in a fast-paced, forward-thinking environment. Apply today and be part of our exciting journey towards enhancing our customer experience through AI!

We are seeking an experienced Full-Stack AI Product Engineer to help build a secure AI-powered business application for regulated organizations. This project involves building a professional AI platform with document analysis, structured AI workflows, knowledge-base integration, user login, admin controls, and downloadable business outputs. This is not a basic chatbot or prompt-only project. We are looking for someone who has built real AI applications, preferably SaaS products, secure portals, or AI tools for business, legal, risk, compliance, financial services, or other regulated environments. Key Skills Required: --Full-stack web application development --AI application development --RAG / knowledge-base architecture --Document upload and document analysis --OpenAI, Azure OpenAI, Anthropic, or similar AI model experience --Vector database experience --Secure user authentication --Role-based access controls --Secure file storage --Admin dashboard development --AI workflow or agent development --PDF, Word, and Excel report generation --Cloud deployment experience --API integration experience --Strong documentation and handoff practices Preferred Experience: --SaaS platform development --Financial services, legal tech, compliance, risk, cybersecurity, or regulated-industry experience --Building AI tools that analyze uploaded documents and produce structured outputs --Enterprise security, data privacy, audit logs, and customer data separation Important Requirements: The selected developer must be comfortable working under an NDA and IP agreement. All platform design, prompts, workflows, templates, scoring logic, documentation, source code, and related work product created for this project will be owned by our company. The developer may not reuse, resell, repurpose, publish, or train other tools using our materials, concepts, client data, workflows, or proprietary information. To Apply, Please Provide: --Examples of AI tools, SaaS platforms, or secure web applications you have built --Your experience with RAG, document analysis, and AI workflows --Your recommended technology stack for a secure AI business platform --Estimated MVP timeline --Estimated cost or pricing structure --Whether you work alone or with a team --How you handle data security, confidentiality, and IP ownership We are looking for someone who can think like a product builder, build securely, communicate clearly, and help create a professional AI platform suitable for regulated business users.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.

I’m looking for a senior AI app developer who can help me build an AI-powered MVP while also guiding me through the technical decisions. This is not just a coding task. I want someone who can think through the product, recommend the right architecture, explain tradeoffs, and build the first working version. The ideal person should be comfortable with OpenAI/LLM integrations, full-stack development, database design, authentication, deployment, and startup-style MVP execution. I’d like to work with someone who can act almost like a technical partner: build the product, teach me what is being done, and help me understand how to maintain or scale it later.

We need a senior architect to design and build a multi-model routing control plane, then lead a small senior team through the build. The control plane sits in front of a family of AI systems and decides, per request (text, image, video), the optimal path across cost, quality, latency, business value, and sovereignty (data residency, rights, and cultural fit): cache and reuse, a small or on-device model, an open-weight, fine-tuned, or sovereign model, or a higher-cost frontier fallback. It routes across compute too: CPU, GPU, inference accelerators, on-device, and edge. Core KPIs: the share of eligible workload kept off frontier accelerators and the resulting cost reduction on a representative workload, plus sovereignty compliance, with no quality regression. This is not a chatbot and not a wrapper over hosted APIs. You own the architecture, define the routing logic, and lead execution. You think in systems, not individual model calls. Context The router is one component of a larger AI platform. It must be model-agnostic: open-weight, fine-tuned, and proprietary models swap in and out behind a stable interface without rearchitecting. A separate team owns the models you route to. The engagement is a 60 to 90 day POC with a working router demo (text-first, with a defined path to image and video), followed by technical leadership through the build. What you'll own Control plane: intake and normalization, classification, routing taxonomy, model-selection logic, fallback hierarchy, cache and reuse rules, telemetry, and the eval feedback loop. Routing that is learned and calibrated, not just static rules: predict per-query difficulty and expected quality, and escalate on confidence thresholds. Comfort with cascades and speculative decoding is expected. Routing across cost, quality, latency, and policy. In constrained environments some requests must stay local regardless of cost. Model-agnostic interface: clean, stable contracts so models and execution paths swap without rework, and the separate model team can work independently of the routing layer. Cost optimization across compute: exact and semantic cache, prefix/KV cache reuse, output reuse, batching, small-model routing, CPU offload, and on-device/edge execution, with a clear fallback hierarchy. The goal is to move most eligible workload off frontier accelerators without degrading output. Generative caching and reuse: caching text is easy; image and video are not, since the same prompt should produce variation rather than an identical result. We need credible reuse at the asset or component level, not just for text. Eval loop: scores output quality by domain and flags weakness so the training team can target fixes instead of retraining broadly. Track quality vs intent, failure modes, cost per route, latency per route, cache hit rate, fallback rate, and regeneration rate. Execution and leadership: architecture blueprint, POC scope, milestones, infra assumptions, and risks leadership can review, plus hands-on architecture review and task breakdown. You'll lead a small senior team, and one of your first deliverables is recommending its exact composition (see screening questions). Ideal background Led or architected production AI infrastructure across several of: multi-model orchestration and LLM routing, multimodal, model serving, inference cost and GPU reduction, CPU and on-device inference, open-source and fine-tuned deployment, cascades and speculative decoding, semantic and prefix caching, eval pipelines, and AI observability. Deployed in at least one constrained environment: on-prem, self-hosted, air-gapped, or data-residency-restricted. You know what breaks when you can't lean on a single cloud. Can lead: set architecture, break down work, review the team's output, and keep the build on track. Tools matter less than the ability to architect the system correctly and lead execution. Not a fit: basic chatbot workflows, hosted APIs only, or prompt engineering alone. Deliverables Control plane blueprint, routing taxonomy, POC plan with milestones and success criteria, and an eval/feedback framework, with a working router demo as the 60 to 90 day target, then technical leadership of a small team through the build. Screening questions The most relevant AI routing, model-serving, or inference infrastructure system you personally designed or built: what was routed, which models or execution paths, and what role did you own? How would you design a router that chooses between cache/reuse, a smaller or local model, an open-weight or fine-tuned model, or a frontier fallback, across CPU and GPU? Where do learned routing, cascades, or speculative decoding fit? For generative image or video requests, how would you approach caching or reuse when the same prompt should still allow variation? Be specific. What metrics and eval loop would you use to prove the router cuts cost without degrading quality, and to help a separate training team find weaknesses? Beyond yourself, what team would you staff to hit these deliverables in 8 weeks? Give the roles, seniority, and headcount, how you'd split the work, and flag any deliverable that 8 weeks and a team of roughly 4 engineers can't realistically cover. To apply Answer the five questions, summarize your most relevant routing or inference-infrastructure work (repos, writeups, talks, or architecture you can describe), and give your high-level approach to a control plane that routes across cost, quality, and sovereignty while preserving quality. Note your availability, your rate, whether you've led a small engineering team before, and the team you'd staff to hit the deliverables in 8 weeks.

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.

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.

Upwork Job Post AI Automation & Performance Marketing Expert Needed for Premium AI Agency Overview We are looking for a highly experienced AI Automation & Digital Growth Specialist to help scale TUFF AI Agents, a premium AI consulting agency that helps businesses automate operations, generate more qualified leads, improve customer experiences, and increase revenue through artificial intelligence. This is a long-term opportunity for someone who understands both business automation and performance marketing. If you have successfully marketed AI services, SaaS products, CRM platforms, automation agencies, or B2B technology solutions—and can demonstrate measurable results—we'd love to hear from you. --- About TUFF AI Agents TUFF AI Agents builds custom AI solutions for businesses, including: - AI Chatbots - AI Voice Receptionists - AI Sales Agents - CRM Automation - GoHighLevel Systems - Zapier Automation - Marketing Automation - Lead Generation Systems - Appointment Booking - Email & SMS Automation - Workflow Automation - AI Business Consulting Our mission is to help businesses eliminate repetitive work, respond faster, improve efficiency, and grow with intelligent automation. --- What You'll Be Responsible For Paid Advertising Plan, launch, and optimize campaigns across: - Meta (Facebook & Instagram) - Google Ads - YouTube Ads - LinkedIn Ads - TikTok Ads - Performance Max - Display & Remarketing Lead Generation Create campaigns that generate qualified consultations through: - Landing pages - Lead magnets - Booking funnels - Retargeting campaigns - Conversion optimization Social Media Marketing Develop and manage content strategies for: - LinkedIn - Facebook - Instagram - TikTok - YouTube Shorts - X Including: - Educational AI content - Case studies - Short-form videos - Client success stories - AI demonstrations - Thought leadership Funnel Optimization Improve every stage of the customer journey by optimizing: - Landing pages - Calls-to-action - Lead forms - Booking rates - Email follow-up - Sales conversion --- Ideal Candidate We're looking for someone with experience marketing: - AI agencies - Automation agencies - SaaS products - CRM platforms - B2B services - Technology consulting - GoHighLevel agencies - Marketing agencies Bonus points if you've worked with: - OpenAI - GoHighLevel - Zapier - HubSpot - Salesforce - AI chatbots - Voice AI - Automation consulting --- Required Skills - Meta Ads Manager - Google Ads - LinkedIn Ads - TikTok Ads - Google Analytics 4 - Google Tag Manager - Conversion Tracking - Landing Page Optimization - Funnel Strategy - Email Marketing - CRM Marketing - Copywriting - Video Ad Strategy - A/B Testing --- Success Metrics Your success will be measured by: - Qualified consultation bookings - Cost per qualified lead - Landing page conversion rate - Return on ad spend (ROAS) - Organic audience growth - Website traffic quality - Lead-to-client conversion rate --- To Apply Please include: 1. Examples of AI, SaaS, or B2B campaigns you've managed. 2. Case studies with measurable results. 3. Industries you've worked with. 4. Typical monthly ad budgets you've managed. 5. Your recommended launch strategy for an AI automation consulting agency. 6. Links to landing pages, funnels, or campaigns you've built (if available). --- Preferred Qualifications - Experience with GoHighLevel - Experience with Zapier or workflow automation - AI marketing experience - B2B lead generation - CRM implementation knowledge - Funnel building - Strong copywriting skills - Video marketing experience Open to hourly or fixed-price proposals based on experience and proven results. We're looking for a long-term partner who can help grow TUFF AI Agents into a leading AI automation consulting brand. If you have a passion for AI, automation, and high-performance marketing, we'd love to hear how you would approach this project.