We are seeking a Mid to Senior-level Fullstack Developer (3–5 years of experience) to not only maintain and expand our platform (Next.js frontend + Django backend) but also guide us in fully integrating AI-powered coding assistance across the full stack. The frontend is already set up for AI-assisted editing (e.g. Cursor), and we want to evolve our backend similarly. The ideal candidate will help us transition the entire development environment, so both we and the developer can leverage state-of-the-art AI coding tools. Beyond coding, you’ll act as a mentor, helping us better understand the backend’s architecture and workflows while integrating AI-driven development tools into our process. You’ll have a key role in making our stack both stable and future-ready, with AI-assisted flexibility. The Context Our frontend was recently ported to Next.js in an ad-hoc manner, leaving certain sections in need of refactoring and stabilization. We have committed to keeping our backend in Django to leverage its robust processing capabilities. You will be the point person for bridging these two environments. Key Responsibilities -Maintenance & Stability: Perform routine maintenance and optimization across the full stack. -Bug Fixing: Identify and resolve issues within the existing Next.js frontend and Django backend. -System Reliability: Provide technical support and troubleshooting during system downtime. -Feature Implementation: Build and deploy new features, ensuring seamless integration between the AI pipeline and the user interface. Required Technical Profile -3–5 years of professional fullstack experience. -Frontend: Proficiency in Next.js and React; experience cleaning up/refactoring "messy" or legacy codebases is a plus. -Backend: Strong experience with Django (Python), specifically working with complex data pipelines or API integrations. -Mindset: A pragmatic approach to coding—balancing the need for new features with the necessity of technical debt reduction.

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.

We’re looking for a strong UX engineer to improve the design, usability, and polish of an existing therapist directory / marketplace product. The product is built in Next.js / React / Tailwind / Supabase. It is already functional, but needs a sharper, more trustworthy, more modern user experience across the public directory, therapist profile pages, search/browse flows, and provider dashboard/admin areas. This is not a greenfield design-only project. I’m looking for someone who can both think through UX and make high-quality frontend changes directly in the codebase. What you’ll work on • Improve the overall visual design and UX of the site • Redesign key pages: homepage/directory, search results, therapist profile pages, provider dashboard • Improve spacing, typography, mobile responsiveness, CTA hierarchy, and trust signals • Make the site feel polished, professional, and conversion-focused • Work in a staging environment with production-like data • Submit clean GitHub commits / PRs Ideal candidate • Strong product/design taste • Excellent React / Next.js / Tailwind skills • Comfortable improving an existing codebase • Can make practical UX decisions without needing everything spelled out • Good eye for healthcare / professional services design • Reliable communicator Tech stack • Next.js • React • Tailwind CSS • Supabase • Vercel • GitHub Deliverables • UX/design improvements implemented in the staging codebase • Responsive desktop/mobile polish • Clear commits or PRs • Brief notes explaining major design decisions

DO NOT SEND AN AI GENERATED PROPOSAL. READ THROUGH THIS POST AND GIVE US YOUR HUMAN RESPONSE. Location: Remote Team size: Small (you’ll know everyone’s name) Stack: Java 21 / Guice / Gradle backend + React 19 / TanStack / MUI frontend **To apply** 1. Give us a few sentences for why you're a fit for the description below. 2. Include a Github/Gitlab/Gitea link to a recent PR you’re proud of (any language) 3. 5–10 minute Loom of you using Claude Code on a real task. **About the role** QAction is a large-scale Java enterprise document, records, and workflow platform — multi-project Gradle build, ~15 modules, customer-specific deployments (USDA, Ditco, Ascension, Mayo). The backend is being modernized behind an OpenAPI v2 surface (oasv2); the frontend is being rebuilt off GWT onto React 19 + TanStack Router/Query/Start with MUI v7. We need a developer who’s equally comfortable extending a v2 endpoint in a Guice-wired Java service as they are wiring up a TanStack Query call behind a Lexical editor or a virtualized data table — and who treats Claude Code as a peer on the keyboard, not a novelty. You’ll work directly with the product lead. No layers, no JIRA theater. Ship, review, iterate. **What you’ll do** Own vertical slices end-to-end: OpenAPI spec → Manager/ManagerImpl + EndpointImpl in QAction Java → React 19 / TanStack Query UI → JUnit + Playwright coverage. Extend the QAction Java backend: Guice (constructor injection only), Hazelcast-aware code paths, customer-deployment-aware module wiring, JUnit under oasv2. Build Modern UI features in the React 19 stack: TanStack Router/Query/Start, MUI v7 + Emotion, React Hook Form + Zod, Lexical, dnd-kit, OIDC auth (oidc-client-ts), TypeScript with the wrapper-component discipline already in place (Button, Icon, LabeledObject, ScrollShadows, etc. — never raw MUI in features). Move features off GWT onto the React 19 stack one slice at a time, without breaking the legacy app. Pair with Claude Code daily — write prompts that ship code, review agent diffs critically, and improve our skills/agents library when the workflow has friction. Keep integration tests honest: real DB + Solr + Keycloak at the seam, not mocks. Tech direction you’ll be living in **Backend** Java 21, Gradle multi-project (build single modules — ./gradlew QAction:build — never clean build) Guice DI, constructor injection, Manager/ManagerImpl naming (we don’t use “Service”) OpenAPI-first v2 endpoints in oasv2, canonical error model, paginated response classes as standalone beans in oasv2.beans Hazelcast clustering, Solr search, Postgres/MSSQL, Keycloak/OIDC FileNet integration, document import/migration tooling, RMT/remote services **Frontend** React 19 + TypeScript, Vite 7, TanStack Router + Query + Start + Table MUI v7 + Emotion, MUI X Date Pickers + Tree View Lexical (rich text), @dnd-kit + Atlaskit pragmatic-drag-and-drop, react-virtualized, react-pdf React Hook Form + Zod, oidc-client-ts + react-oidc-context, notistack, pino File naming kebab-case, @/ path imports, theme-first styling, wrapper components over raw MUI Adjacent surfaces in scopes over time **What we’re looking for** 8+ years shipping production app code across backend and frontend. You’ve owned services and UIs, not just one or the other. Java fluency — Guice or Spring DI, REST/OpenAPI design, JUnit. Bonus if you’ve worked in a 1M+ LOC enterprise Java codebase with customer-specific module deployments and didn’t flinch. Modern React fluency — React 18/19, TypeScript, TanStack Query (or React Query), a real opinion about effect dependency graphs, comfort with virtualized tables and rich-text editors. OpenAPI-first thinking. Specs are the source of truth, not documentation written after the fact. Claude Code power user. You write prompts that delegate well, you know when to use a subagent vs. inline, you’ve built or extended skills/hooks/MCP integrations. You can show us a recent session where Claude shipped something non-trivial under your direction. Small-team temperament. You’re allergic to ceremony. You read code before asking. You raise a small PR when it’s ready. **Nice to have** GWT (you won’t write new GWT, but you’ll need to read and migrate it). Workflow / BPMN, records management, e-signature, or ECM domain experience. Hazelcast, Solr, Keycloak operational familiarity. Lexical or another modern rich-text editor. Playwright (you’ll inherit a calibrated suite — we want you to improve it, not rewrite it). Docker compose stack bring-up across Postgres, MSSQL, Solr, Keycloak. Python services or Helm/Kubernetes deployment experience. **How we work** Small PRs. Every change ships through review. If it’s getting big, split it. Test-driven design. Tests come with the code, not after. JUnit on the Java side, Vitest + Playwright on the React side. Daily standups. 15 minutes, async-friendly, but we show up. Memory-backed Claude Code sessions — agents learn the codebase with you, not against you.

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

# Full-Stack AI Engineer — Semantic Search + Next.js + Supabase (Long-Term, Contract-to-Hire) ## About We're building an AI-native platform that makes a large archive of recorded talks genuinely discoverable and useful: need-based semantic search over transcribed media, with a subscription product built around it. We have a clear product vision and architecture and are looking for a lead engineer to build the first version and grow with us long-term. Full product details are shared with shortlisted candidates under NDA — this post focuses on the engineering and the skills we need. ## The engineering challenge You'll build a two-part system that shares one database: 1. **A content pipeline (Python):** ingest recorded talks, transcribe them, chunk and enrich the transcripts with metadata using an LLM API, generate embeddings, and store everything in Postgres. 2. **A web app (Next.js):** fast, crawler-friendly, SEO-strong content pages with structured data; retrieval-based search that returns relevant source material with links/citations; user accounts; and Stripe-gated paid content. We care a lot about retrieval *quality* and clean, maintainable architecture — this is a real product, not a prototype. ## Required tech stack - **App:** Next.js (App Router), TypeScript, Vercel. Strong SSR/SSG, SEO, and JSON-LD structured-data experience. - **AI/backend:** Python; production RAG (embeddings, chunking, retrieval quality); LLM API integration. - **Data:** Postgres + **pgvector** (via Supabase); embeddings via a hosted model (Voyage/OpenAI). - **Auth & gating:** Supabase Auth with row-level security. - **Payments:** Stripe (subscriptions + one-time). ## Required skills - Shipped production Next.js (App Router) + TypeScript apps with strong SSR/SEO. - Built a real RAG / vector-search system in production — not a tutorial clone. - Comfortable in Python for data pipelines. - Postgres + pgvector and Supabase in production. - Stripe integration. - Plans before building; communicates clearly in writing. ## Nice to have - Audio/video transcription experience (Whisper / faster-whisper / Deepgram / AssemblyAI). - Agentic coding workflows (e.g., Claude Code). - Content-heavy SEO products or media libraries. ## Engagement - Hourly, contract-to-hire. ~20–40 hrs/week to start; long-term for the right person. - We start finalists on a **small paid test project** (a single self-contained slice of the pipeline) before the full engagement — that's how we evaluate fit. ## Confidentiality This is a proprietary product. Shortlisted candidates sign a mutual NDA before we share full scope and context. Please don't expect complete product details in the first exchange — strong technical applicants will have everything they need to be evaluated, and the rest follows the NDA. ## How to apply Applications that skip these are ignored: 1. **Start your proposal with the word `pgvector`** so we know you read this. 2. Link **two** projects: one live Next.js/SSR app, and one RAG/embeddings or LLM-integration project. Tell us what *you* personally built. 3. Answer briefly: *An offline embedding pipeline and a live search query must use the same embedding model — why does that matter, and how would you guarantee it?* 4. One line on your approach to chunking long-form audio/video transcripts for good retrieval.

Full-stack developer (Node.js/TypeScript, React) with cloud/DevOps experience, and comfortable with PostgreSQL, S3-compatible storage, and Google Cloud service accounts. This is to support solid mobile app built for dual platforms, with a full ML comparison pipeline, and the supporting infrastructure. Initial Task will be an infrastructure migration (DevOps). Second task will be several front end and backend app updates.

DataSci Technologies, Inc. is an enterprise software C-Corp. We own our domain (datascitechnologies.com) and have finalized our independent institutional asset valuation. We are seeking an elite, Expert-level Full-Stack Product Architect / Fractional CTO to build the V1 / MVP layout of our proprietary predictive AI platform, the DST Codex, from the ground up. The Product & Target Audience: The DST Codex is a secure, high-end portal built specifically for institutional asset management firms and real estate conglomerates managing $250M to $10B+ in AUM [1.1]. The platform maps out, quantifies, and visualizes hidden operational risk metrics—known as "Shadow Variables"—to protect institutional capital. The user interface must look like a premium, dark-mode institutional banking terminal. Required Technical Scope (Full Lifecycle Build):Infrastructure & Security: Secure database architecture setup using AWS or Google Cloud Platform with enterprise-grade encryption. Front-End & Interface: Building a pristine, highly aesthetic, dark-mode responsive dashboard layout (React, Vue, or enterprise visual frameworks like WeWeb/Bubble). The Core Visualization Layer: Engineering interactive, custom open-source data tracking charts (utilizing D3.js or high-performance visualization packages) to map out predictive risk telemetry. User Auth: Airtight, password-protected multi-tenant user authentication and client login portals. Project Structure: We are a capital-efficient C-Corp moving at high velocity. We are looking for an experienced product builder who can act as a long-term technical partner. Please share your portfolio of completed SaaS platforms/dashboards, outline your architectural recommendations, and state your standard fixed-price or monthly fractional structures for a 60-day development milestone sprint.

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

Expectations include setting up development, staging and production environments in AWS, creating and managing the CI/CD pipeline and support for front end development and integration with the API.