## 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.

I’m looking for an AI Engineer to help build an automated red-teaming product based on open-source models. This is a short-term, hands-on project for around 2 months, with an expected commitment of about 20 hours per week. The goal is to build a specialized red-teaming engine that can generate adversarial prompts across different risk domains, severity levels, and attack strategies — then automatically run those prompts against target AI models to identify bad cases, failure patterns, and safety gaps. 🔍 What you’ll work on Build red-teaming systems on top of open-source LLMs, including fine-tuning, prompt optimization, evaluation pipelines, and model orchestration. Design automated prompt generation workflows across risk domains such as self-harm, hate, violence, sexual safety, misinformation, fraud, cyber, and other high-risk areas. Generate prompts across different harm levels, from benign edge cases to policy-borderline and clearly unsafe scenarios, while maintaining structured taxonomies and evaluation criteria. Run automated tests against target models such as Gemma, Llama, Qwen, or other open-source / closed-source models to surface jailbreak patterns, over-refusal, under-refusal, and policy inconsistencies. Build feedback loops that turn model failures into stronger red-team prompts, improved eval sets, remediation recommendations, and continuous safety testing. 🧠 What I’m looking for Hands-on experience with open-source LLMs, fine-tuning, LoRA / QLoRA, RAG, model evaluation, and LLM inference pipelines. Familiarity with AI safety, red teaming, adversarial prompting, jailbreaks, safety evals, or trust & safety systems. Ability to build end-to-end systems, including data pipelines, model serving, eval harnesses, scoring, dashboards, and automation workflows. Bonus if you’ve worked on model safety, content moderation, policy evaluation, agentic testing, or automated eval infrastructure. ⏳ Project setup Duration: around 2 months Time commitment: about 20 hours per week Format: flexible / remote-friendly Stage: early-stage build, from 0 to 1 🚀 Why this is interesting This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate, test, categorize, and learn from model failures — helping teams understand where AI models break, why they break, and how to improve them. If you enjoy working with open-source models, AI safety, red teaming, and fast 0-to-1 product building, I’d love to chat. Feel free to DM me if this sounds like you, or if you know someone who might be a good fit.

Join our family-owned janitorial supply distributor in Rahway, NJ, established in 1961. We are seeking an experienced developer to integrate Claude AI into our enterprise systems. The role involves developing custom solutions, ensuring seamless integration, and optimizing AI performance. Collaborate with our team to enhance our operations and customer experience.

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 

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 are seeking a hands-on AI systems expert to help us establish, secure, and scale our internal AI capability. The role involves both technical implementation and advisory responsibilities, with the expectation of staying on as a trusted advisor. The ideal candidate will have a strong background in AI systems and be able to provide strategic guidance.

Summary We are deploying a 100% offline, private AI stack using LibreChat, vLLM, and RAG. The project requires setting up a local infrastructure for AI model deployment, ensuring security and privacy. The ideal candidate will have experience in MLOps and DevOps, with a focus on local AI deployments. Responsibilities include managing the deployment process, optimizing performance, and ensuring the system's reliability and security. Title: MLOps/DevOps Engineer Needed to Deploy Private, Local AI Stack (LibreChat + vLLM + RAG) Project Description: We are deploying a 100% offline, privacy-first AI portal on local hardware (Threadripper server with Dual RTX 6000 Blackwell 96GB GPUs). Must be able to work at least in a hybrid environment, preferably in-person at our NYC office. We need an expert to containerize and automate this infrastructure. The stack consists of: LibreChat frontend, MongoDB, MeiliSearch, local HuggingFace RAG, and local SLMs (Llama 3/Mistral) served via vLLM. Key Responsibilities: Configure Ubuntu server host, including NVIDIA drivers and CUDA environment. Optimize multi-GPU serving utilizing vLLM for high-throughput local inference. Build out a localized, containerized RAG pipeline with HuggingFace. Write Docker Compose and Ansible configuration scripts for automated deployment.

We're building an internal AI system that runs entirely on our own hardware (no cloud inference) against our own company data. We have a working proof-of-concept and want to get the architecture right. We need an experienced consultant to review what we've built, pressure-test our decisions, and tell us where we're wrong. This is an advisory/validation role first — we have someone doing the hands-on work; what we want is a senior second opinion to make sure we're building this the right way. What we're running today: Inference: RTX 5090 (32GB, Blackwell), Ubuntu 24.04, running llama-server (llama.cpp + CUDA) serving Gemma 4 31B-it (Q4_K_M GGUF) at a 262,144 context window. Also hosts our MCP retrieval server, PostgreSQL, and Qdrant. Embeddings: separate machine with an RTX 3060 running vLLM serving Qwen3-Embedding-4B. RAG: hybrid retrieval — Postgres full-text search + Qdrant semantic search with RRF fusion, exposed through a custom MCP server with tool-calling. Data: ingesting our own internal operational data into Postgres + Qdrant. Planned stack: LiteLLM for model routing, n8n for automation, Open WebUI for the interface, Langfuse for observability, Vault or Infisical for secrets, Keycloak/Azure AD for SSO. What we need help with: Validating our two-machine split (inference vs. embeddings) and whether our VRAM/context budget holds up under real load — specifically whether a 256K context window is real and performant on a single 32GB card or just nominal. Model selection and routing strategy: which open-weight models for which tasks, and how to structure LiteLLM routes. RAG quality: chunking, embedding dimensionality, hybrid search tuning, reranking — making retrieval actually accurate on messy real-world data. Sanity-checking our overall architecture and telling us our blind spots. You should have done: Stood up local LLM inference in production — llama.cpp/llama-server and vLLM, not just Ollama on a laptop. You understand GGUF quantization (Q4_K_M, IQ-series), KV cache, KV-cache quantization, and how context length maps to actual VRAM consumption. Real fluency in GPU sizing math — given a model, a quant, and a context window, you can tell us whether it fits on a given card and what throughput to expect. Bonus if you've worked with Blackwell / sm_120a. Built production RAG — vector DBs (Qdrant, pgvector), hybrid search, RRF fusion, embedding model selection, reranking, evaluation. Worked with agentic/tool-calling systems and ideally MCP servers. Know the open-weight model landscape (Gemma, Qwen, Llama, Mistral, Phi, Nemotron, Hermes) and their licenses well enough to advise. Production ops: systemd, Docker, model gateways (LiteLLM or similar), observability (Langfuse), secrets management, SSO.

**** 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.

We are seeking an experienced IT professional with expertise in Microsoft 365 Enterprise to help us evaluate, configure, and implement a secure AI environment for our organization. Our goal is to leverage AI capabilities within the Microsoft ecosystem while ensuring all client materials and confidential information remain protected and are not exposed to public AI models. We are specifically interested in understanding and implementing the appropriate Microsoft 365 Enterprise (E3/E5), Microsoft Copilot, Azure OpenAI, Entra ID, Purview, and related security tools to create a secure, self-contained AI environment. Requirements: Strong experience with Microsoft 365 Enterprise administration and security Knowledge of Microsoft Copilot, Azure OpenAI, Entra ID, Microsoft Purview, and Data Loss Prevention (DLP) Experience implementing secure, enterprise-grade AI solutions Ability to configure security, governance, permissions, and access controls Familiarity with Microsoft 365 Enterprise licensing and security features Please include a brief summary of your relevant experience and any similar projects you have completed.