-$150K Salary with Healthcare benefits, W2 applicants ONLY. -Must be U.S. Citizen -Remote role in U.S. with 25% travel to client sites Show me your best work as a Forward Deployed AI Engineer (FDE) by showing me: a) how you approach enterprise clients' complexities ($1B+ revenue businesses)** experience is a must with enterprise b) your technical fluency across data, tech, AI c) your people skills d) how you break down business processes e) experience around customer experience work preferred** Claude Certified Architects, Codex, and all other LLMs/AI engineering tools Only interviewing serious candidates looking for full-time work. No agencies, or LLCs/s-corps.... only FTEs W2. ex-Big 4, Big Tech Preferred candidates will get interviews first. Applicants that use AI slop to apply won't be considered.

I'm early in my career and self-taught, working on breaking into AI engineering, and I want one honest hour with someone actually in the trenches — not a sales pitch, not encouragement. Just a candid conversation, video on. What I'm after: your unvarnished read on how someone in my position actually breaks in, where the field is really headed, what companies are truly paying to have built or solved, and — if you were starting out today with what you know now — exactly what you'd do over the next 6 months. About me: no CS degree, no traditional pedigree — I've been learning by shipping real projects (I've built a couple of small live AI products end-to-end: RAG, an agent, automation). I know enough to ask good questions and I'll come prepared so we use the time well. I'd also love to show you one thing I built for 60 seconds of brutally honest feedback.

The Problem We deal with a high volume of documents in mixed formats — PDFs, scans, spreadsheets, emails with attachments — and we're tired of manually pulling data out of them. We need an AI-powered agent that can take a messy pile of documents and turn them into clean, structured, usable data. What We Need Built A Python-based document processing pipeline powered by Claude API that can: - Accept documents in bulk (PDF, images, DOCX, CSV, email exports) - Automatically detect what type of document it's looking at - Pull out the relevant fields based on document type - Output structured JSON/CSV ready to feed into our database or CMS - Flag anything it's not confident about instead of guessing This isn't a chatbot or a content generator. It's a back-end workhorse that needs to be accurate, fast, and cost-effective at scale. Scope & Budget Total: $500 fixed price Milestone 1 — Core Engine — $250 - Document intake and format handling (OCR for scans, text extraction for native PDFs, parsing for office files) - Classification layer that routes documents to the right extraction logic - Field extraction using Claude API with structured outputs - Confidence scoring and human review flagging - Deterministic checks for predictable fields (dates, currency, IDs) — don't burn API calls on things regex can handle - Error handling that isolates failures to individual fields, not entire documents - Cost controls — smart model selection, batching, and caching where it makes sense Deliverable: working pipeline demonstrated on our test document set, with accuracy metrics. Milestone 2 — Two Domain-Specific Modules — $125 each Once the core works, we bolt on two specialized modules for specific document categories we process regularly. Each module gets its own field mappings, validation rules, and output schema. Details shared after Milestone 1. Skills Required - Claude API / Anthropic SDK (structured outputs, vision, token management) - Python (document parsing, OCR, automation) - PDF / OCR tooling (PyMuPDF, Tesseract, pdf2image) - Data extraction and validation pipelines - JSON/CSV structured output design When You Apply Skip the sales pitch. Tell me: 1. What's the messiest document processing problem you've solved with AI, and what made it hard? 2. Walk me through how you'd handle a scanned PDF where OCR gives you garbled text on a key field. 3. How do you keep API costs under control when processing hundreds of documents per day? Ongoing work available if this goes well.

I’m looking for an AI Engineer to help build an AI Safety Evaluation & Governance product powered by open-source models. This is a 1-month, hands-on project with an expected commitment of around 20 hours per week. The goal is to build an MVP that can automatically test AI models, identify safety failures, analyze failure patterns, and support continuous improvement. 🔍 What you’ll work on • Build an automated red-teaming engine that generates test cases across risk domains, severity levels, and attack strategies • Run tests against models such as Gemma, Llama, Qwen, and API-based models • Develop evaluators for jailbreak success, policy violations, over-refusal, under-refusal, and severity • Structure safety policies into consistent taxonomies and evaluation criteria • Turn confirmed failures into reusable eval datasets and regression tests • Build lightweight reporting for model comparison, human review, and policy-version tracking 🧠 What I’m looking for • Experience with open-source LLMs, inference pipelines, prompt optimization, fine-tuning, LoRA/QLoRA, and LLM evaluation • Ability to independently build an end-to-end MVP, including data pipelines, model orchestration, scoring, and reporting • Familiarity with AI safety, red teaming, jailbreaks, content moderation, or Trust & Safety systems • Bonus: experience with model-based evaluators, human-in-the-loop review, agentic testing, or multimodal safety ⏳ Project setup Duration: 1 month Time commitment: Around 20 hours per week Format: Flexible and remote-friendly Stage: Early-stage, 0-to-1 MVP This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate tests, evaluate model behavior, identify safety gaps, and verify whether issues have been resolved. If this sounds like you, please DM me with a brief introduction and examples of relevant work.

We're building an AI Research Copilot for our financial analytics platform. The Copilot will help investors and financial professionals analyze market data, company financials, news, and earnings using natural language. This is a production AI project, not a basic chatbot. Responsibilities Build a production-ready AI Research Copilot Design and implement RAG pipelines Integrate LLMs with our financial data and APIs Implement semantic search and tool calling Build conversation memory and streaming responses Add source citations and improve response accuracy Optimize performance and reduce hallucinations Required Skills Python OpenAI, Claude, or Gemini APIs RAG and vector databases (Pinecone, Qdrant, pgvector, Weaviate, etc.) LangGraph, LangChain, LlamaIndex, or similar frameworks API integration Prompt engineering Production AI application experience Please include: AI products or copilots you've built Your experience with production RAG systems Your preferred AI architecture for this project Briefly explain how you reduce hallucinations and provide trustworthy AI responses. We're looking for an experienced engineer who can build scalable, production-quality AI systems and collaborate long term.

The objective is to develop an AI model that detects and classifies Longitudinal and Transverse Cracking (LTC) in asphalt airfield pavements, in accordance with ASTM D5340-24. The model will use fused Digital Elevation Model (DEM) and imagery data to identify cracks, measure their extent, and assign a severity level (Low, Medium, or High) based on the standard's width and spalling criteria. The output will support the broader Pavement Condition Index (PCI) workflow used across the company's 85-model system. What you'll need to do: • Access and understand the provided DEM and imagery data, along with existing labels • Build a pipeline that fuses DEM and imagery to detect cracks and measure their length and width • Train a model to classify each detected crack as Low, Medium, or High severity per the ASTM thresholds • Apply the standard's exclusion rule so distresses are not double-counted where other cracking is already recorded • Validate the model's outputs against the team's verified ground truth and report accuracy • Deliver the trained model along with a short write-up of results and any limitations

We are looking for an experienced Full-Stack AI Developer to build and improve modern AI-powered web applications. The ideal candidate should have strong experience in frontend and backend development, AI integrations, LLMs, LangChain, RAG pipelines, vector databases, APIs and scalable system architecture. Required Skills: * React.js, Next.js and TypeScript * Node.js and Python * LangChain, LLMs and RAG * OpenAI, Claude or similar AI APIs * Vector databases such as Pinecone, Qdrant or Weaviate * REST APIs, databases and third-party integrations * Strong knowledge of software architecture and clean code * Experience with Claude Code, Cursor or similar AI development tools * Excellent English communication and problem-solving skills Please share your GitHub profile, relevant AI projects and a short Loom video explaining one live Full-Stack AI application you have built.

Project OverviewWe are seeking a highly skilled, US-based Lead Developer for a 4-to-6-week project building an AI-driven sales automation layer. The core objective of this system is to automate administrative sales tasks—such as top-of-funnel research, routine quoting, and follow-ups—allowing the sales team to focus on high-value conversations. The system will operate seamlessly within our enterprise CRM environment and rely on strict human-in-the-loop approval processes for all external actions. Core ResponsibilitiesYou will lead the development of specialized AI agents designed to handle specific workflow tasks, including:Lead Generation & Research: Integrating data provider APIs and language models to identify target companies and auto-generate preparatory account briefs. Automated Outreach & Tasking: Building systems to draft personalized sequences, categorize incoming request intent, and automatically sync replies or tasks directly to the CRM. Quote & Order Automation: Developing logic that categorizes incoming requests, drafts standard quotes using historical CRM pricing data, and stages approved orders for processing. Retention Modeling: Creating an agent that analyzes historical order cadences to flag dormant accounts and automatically draft personalized check-ins for the sales team to review. Required Technical StackLocation: Must be located in the United States.CRM Integration: Deep expertise working with enterprise CRM APIs (REST, custom records, integration roles); specific experience with NetSuite is highly preferred. AI/LLM Implementation: Proven track record of building applications using Large Language Models, specifically involving retrieval-augmented generation (RAG) over private, structured datasets like product catalogs and past orders. Architecture: Strong ability to translate captured business rules into testable code and build secure, user-friendly approval queues. Project PhasingTimeline: 4 to 6 weeks.Phase 1: Collaborative scoping and process mapping to finalize agent specifications, identify data fields, and set integration endpoints. Phase 2 & 3: Core build phase in a sandbox environment utilizing historical data replays, followed by parallel testing against current human workflows before any live deployment. Does this capture exactly what you need the developer to focus on, or should we also remove the mention of "parsing inbound order-desk emails" from the quote/order workflow

AI SYSTEMS ENGINEER Agentic AI, Multi-Agent Systems & Secure AI Workflows (U.S.) Remote • United States We're building production AI systems designed for enterprise environments. We're looking for exceptional AI systems engineers who enjoy solving difficult systems problems – not just writing code. Our work sits at the intersection of agentic AI, software architecture, enterprise systems, governance, security, and operational intelligence. We design AI systems that improve how organizations operate while meeting the standards required for production deployment. We value engineers who think in systems, challenge assumptions, and care deeply about building technology that is reliable, understandable, secure, and useful. If you're motivated by difficult engineering problems, thoughtful architecture, and building production AI systems for enterprise organizations, we'd like to hear from you. WHAT YOU'LL HELP BUILD Examples of the types of systems we design include: - Multi-agent AI systems - Enterprise AI assistants - Secure AI workflows - Enterprise workflow automation - AI-powered knowledge systems - Human-in-the-loop decision support - Document intelligence - Retrieval-Augmented Generation (RAG) - AI memory and retrieval systems - AI evaluation and testing frameworks - Secure enterprise AI platforms - AI governance capabilities - Operational intelligence platforms TECHNICAL EXPERIENCE WE VALUE We're interested in engineers with experience in some combination of: - Python - AI Agent Development - LangGraph - LangChain - Large Language Models - API Development - Vector Databases - Software Architecture - Enterprise Systems Integration - Information Security Experience with OpenAI, Anthropic, Model Context Protocol (MCP), cloud infrastructure, workflow orchestration, observability, distributed systems, or regulated technology environments is also valuable. We do not expect expertise in every technology. We care far more about engineering judgment, systems thinking, demonstrated execution, and continuous learning than checking every technology box. THE PROBLEMS WE ENJOY SOLVING The engineers who thrive here enjoy questions like: - How should multiple AI agents coordinate work? - How should humans remain in control of important decisions? - How should production AI systems scale safely? - How should memory be designed for enterprise AI? - How should AI systems balance operational performance with governance, security, and reliability? - How should AI systems create measurable business value? If those questions excite you, you'll probably enjoy working with us. WHAT MAKES SOMEONE SUCCESSFUL HERE We're looking for engineers who: - Think in systems rather than individual features. - Care deeply about production quality. - Enjoy solving ambiguous technical problems. - Communicate complex ideas clearly. - Balance speed with sound engineering judgment. - Build practical solutions rather than chasing hype. - Continuously learn, experiment, and improve. We're significantly more interested in systems you've built than technologies you've used. Please provide specific examples that demonstrate your role, engineering decisions, and measurable outcomes. We recognize that many engineers use AI as part of their workflow. You're welcome to do the same. However, your application should accurately reflect your own experience, judgment, and technical thinking. We respect the confidentiality of your current and former employers, clients, and partners. Please do not include proprietary or confidential information in your application. Describe your work at a level that demonstrates your engineering approach without disclosing protected information. PROFESSIONAL STANDARDS We value integrity, sound engineering judgment, and respect for intellectual property. Please do not include confidential, proprietary, export-controlled, or other non-public information belonging to your current or former employers, clients, or partners in your application or work samples. We're interested in your engineering approach, architectural thinking, and problem-solving methodology, not protected information belonging to others. If you share code, architecture diagrams, technical documentation, or project examples, please ensure you have the legal right to do so and identify any material open-source or third-party technologies where appropriate. By submitting application materials, you represent that you have the legal right to share them and that doing so does not violate any confidentiality, intellectual property, employment, consulting, or other contractual obligations. Any engagement, if offered, will be subject to a separate written agreement covering confidentiality, intellectual property ownership, compensation, and other applicable terms. Submission of an application or participation in the evaluation process does not create any employment, independent contractor, partnership, joint venture, agency, fiduciary, or other business relationship with 26ers AI, nor does it obligate either party to enter into any future engagement. 26ers AI reserves the right to evaluate applications, discontinue discussions, modify the hiring process, or decline to pursue any engagement at its discretion. Nothing in this posting should be construed as an offer of employment or an offer to contract.

We’re an early-stage startup looking for an experienced AI SaaS / AI Agent / Automation Developer to help architect a new project from the ground up. The initial scope is to review our idea, define the technical architecture, recommend the right AI/automation stack, and create a clear roadmap for MVP development. We’re looking for someone with hands-on experience in: AI SaaS & AI Agent architecture LLMs, APIs, RAG & agentic workflows n8n / Make / custom automation Backend, database & third-party integrations Designing scalable, production-ready AI systems Initial engagement: Project architecture & technical planning, with potential to continue into MVP development. Please share relevant AI projects you’ve architected or built and briefly explain your recommended approach for starting a new AI SaaS project.