We’re seeking a talented AI Narrative Prompt Designer to design the intelligence behind Guroute’s storytelling engine. This is not a traditional prompt design role, as it involves creating a dynamic narrative system that can adapt to user interactions. The ideal candidate will have experience in AI and narrative design, with a strong understanding of storytelling principles and AI technologies.

AI Workflow & Automation Consultant for Executive Search Firm We are an executive search firm looking for an experienced AI technology consultant to help us evaluate, improve, and automate our internal workflows. We are not looking for someone to simply introduce generic AI tools. We want someone who can take a hands-on look at how our business currently operates, understand our recruiting/search process, and help us create practical, efficient AI-enabled workflows that our team will actually use. About Our Current Tech Stack We currently use: RecruitCRM as our applicant tracking/search CRM Microsoft 365 across the business Microsoft Copilot as our enterprise AI tool Claude, which integrates with RecruitCRM Microsoft Teams, Outlook, OneDrive, SharePoint, Word, Excel, and PowerPoint Additional recruiting tools, interview notes, candidate documents, and internal templates We want to better connect and leverage these systems so we can reduce manual work, improve consistency, and make our team more efficient. What We Need Help With We are looking for someone to review our full process and help identify where AI, automation, integrations, or better workflows can improve the way we work. This may include: Reviewing our current recruiting/search workflow from intake through candidate presentation Evaluating how we use RecruitCRM, Copilot, Claude, Microsoft 365, Teams, Outlook, OneDrive, and SharePoint Identifying repetitive manual tasks that can be automated or streamlined Helping us create better workflows for candidate presentations, interview notes, client updates, reporting, and document management Recommending practical AI tools, prompts, templates, automations, or integrations Helping us organize files, notes, candidate materials, and client deliverables more efficiently Training or documenting best practices so our team can adopt the new workflows Ideal Background The ideal consultant will have experience with: AI workflow design for small or mid-sized businesses Microsoft 365, Microsoft Copilot, Teams, Outlook, OneDrive, and SharePoint Recruiting, staffing, executive search, or professional services workflows CRM or ATS systems, ideally RecruitCRM or similar platforms Claude, ChatGPT, Copilot, or other AI tools used in business operations Automation tools such as Power Automate, Zapier, Make, or similar Process mapping, workflow optimization, and implementation Project Goal Our goal is to have someone come in, understand how we work today, and help us build a more efficient AI-enabled operating system for the business. We want practical improvements, not theoretical recommendations. The right person should be able to assess our current state, recommend what should change, help implement improvements, and provide clear documentation or training for our team. Deliverables May Include Current workflow assessment AI and automation opportunity map Recommended tools and integrations Updated workflows for key business processes Reusable prompts, templates, or SOPs Microsoft 365 / Copilot / RecruitCRM optimization recommendations Implementation support Training documentation for our team To Apply Please include: A brief overview of your experience with AI workflow consulting Examples of similar projects you have completed Your experience with Microsoft 365, Copilot, Claude, RecruitCRM, or recruiting workflows How you would approach reviewing and improving our current process Your suggested project structure and estimated timeline We are open to either an initial audit/strategy project or a longer-term implementation engagement depending on fit.

Need someone to build me an CFO ai agent that can analyze monthly P&Ls and Balance Sheets from multiple different businesses within a franchised system. The financials will be in PDF format. Also need the ai agent to transfer funds from checking accounts to PayPal every week (I currently do this manually so it's setup, just want it automated)

I am looking for an experienced ASR engineer to build a production-ready speech-to-text system for a low-resource language. I already have approximately 3,000 prepared audio segments, totaling about 10 hours of audio, with clean and consistent transcripts ready for immediate use. Data preparation and segmentation are already handled. The initial 10 hours of audio will serve as the first milestone. After that, the engineer will be expected to continue training and improving the model with additional data until the system reaches a target WER of 10% or below. Your responsibility will focus on: Fine-tuning a Whisper-based model for high transcription accuracy Optimizing word error rate (WER) over time Providing inline/embedded start timestamps per phrase Building an efficient inference pipeline for both real-time and batch transcription Structuring evaluation and improvement workflows Preparing the system for deployment and integration into a web platform Providing clear documentation and guidance so I can independently continue training and improving the model over time without ongoing engineer involvement The goal is to reach strong accuracy at launch, with a clear process for continued improvement as more data becomes available. Please describe your experience with Whisper fine-tuning or similar ASR model training in your proposal.

Senior AI/ML Engineer / Claude architect — Legal Tech FirmProfit AI is the operational backbone of the modern law firm. We automate law firm operations end to end, and we're looking for a top-tier AI/ML engineer to help us build the next major platform in legal tech. We need a true expert. Someone deeply proficient with Claude and modern LLM architecture who has shipped real products at a high level. You're fluent across the full stack with Node.js, React, Postgres, MongoDB etc... and you have hands-on experience building with LangChain, LangGraph, MCP, and AWS Bedrock. We're not looking for someone who's read about LLMs. We're looking for someone who has shipped agents, orchestration layers, and production AI systems that real users depend on every day. Our current team is 8 engineers, we have firms signed and live, and we're moving fast. This is a chance to come in early, and have your work in the hands of customers within weeks. Contract to start, with a long-term path for the right person. Reply with the most impressive AI product you've shipped.

NobleProg is seeking an experienced AI Trainer to deliver a live, instructor-led remote training focused on helping technical professionals integrate Agentic AI and RAG systems into their existing workflows. This opportunity is designed for participants with strong technical backgrounds (Data Engineering and Workflow Automation) but limited formal AI experience, with the goal of applying AI to real-world systems rather than learning theory. Engagement Details Location: Remote Duration: 2 days Audience: Data Engineers and Workflow Developers Participants: 4+ Daily Rate $1,100 per day Course Scope This training focuses on practical, hands-on development of AI-powered systems using Retrieval-Augmented Generation (RAG) and agent-based architectures. The course will follow a Core & Split approach, starting with shared foundational concepts, moving into role-specific deep dives, and concluding with an integrated session demonstrating how AI systems are built and applied across workflows and data pipelines. NobleProg SOP - https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Key Topics Include - Practical introduction to LLM applications and AI system architecture - Retrieval-Augmented Generation (RAG) design and implementation - Data preparation, embeddings, and vector database concepts - Agentic AI fundamentals (tools, decision-making, multi-step workflows) - Orchestration frameworks such as LangChain, LangGraph, or similar - Role-based applications: RAG pipelines for data engineers and AI-driven workflows for workflow developers - End-to-end system integration (RAG + agents + automation) Trainer Responsibilities - Deliver engaging, instructor-led remote training with strong hands-on focus - Translate AI concepts into practical applications for non-AI technical professionals - Structure delivery using a Core & Split model to address different roles - Provide real-world exercises aligned with data pipelines and workflow automation - Facilitate an integrated session demonstrating how different components work together - Prepare training materials (trainer retains ownership of content) Required Qualifications - Hands-on experience building LLM-based applications, including RAG systems and agent-based workflows - Strong proficiency in Python and experience with APIs, data pipelines, or automation systems - Experience with frameworks such as LangChain, LangGraph, or similar - Proven experience delivering technical training to engineering audiences - Ability to simplify AI concepts and connect them to real-world use cases Nice to Have - Background in data engineering, workflow automation, or solutions architecture - Familiarity with MCP or emerging agent orchestration frameworks - Experience designing modular or role-based training programs preferred - Experience building production-grade AI applications preferred https://docs.google.com/document/d/184VlJipyixkLNJ_HnP3aPt4YToedTUAlji_LxkuLhRU/edit?usp=sharing Please review and approve this tentative outline. We will be meeting with the client to determine whether they prefer a 1-day or 2-day delivery format. The agenda may require some adjustments based on the client's specific objectives, technical background, and areas of interest, which can be finalized during the trainer-client consultation call. Could you please review the proposed outline and let us know if you see any red flags, gaps, concerns, or topics that may require immediate attention? We would also appreciate any recommendations regarding scope, level of technical depth, hands-on exercises, or prerequisite knowledge that should be addressed before presenting this to the client. Thank you for your feedback. How to Apply Please include - A brief overview of your experience with Agentic AI and RAG systems - Your experience delivering technical or AI-focused training - Examples of AI systems or applications you have built - Your approach to teaching participants without formal AI background - Availability for remote delivery

We're building an AI health companion for women's health and need an experienced AI Architect for weekly consulting sessions (3-5 hours/week). What You'll Do Meet with our team 1-2x per week to review architecture and provide technical guidance Help optimize our AWS Strands/RAG integration for latency, cost, and scalability Advise on conversation management, context handling, and orchestration decisions Guide us through key technical tradeoffs as we move from prototype to production Our Stack Django backend, Flutter frontend, AWS Strands What We Need 5+ years with AI/ML in production, especially RAG/LLM integration and orchestration Experience with AWS Strands and Bedrock Track record with conversation AI architecture and scaling constraints Bonus: healthcare/HIPAA experience, startup advising, Django/Python knowledge Details 3-5 hours/week, flexible remote schedule Initial 3-month engagement Mix of live calls and async reviews

About the Role: We are seeking a highly qualified Senior Machine Learning and Natural Language Processing Engineer with deep expertise in sentence parsing, contextual understanding, categorization, and language extraction to support and advance Sybal’s Proof of Governance® (PoG™) platform. This role blends advanced NLP engineering, full-stack development, and enterprise-grade deployment. You will design custom NLP models, build scalable AI-driven services, and deploy production-ready applications that transform raw policy and technical language into structured governance intelligence. You must be a senior-level full-stack engineer proficient in Python, Django, JavaScript, HTML, and CSS, with the ability to dockerize and deploy applications into production environments. Experience commercializing enterprise AI applications is required. You should also be familiar with using agentic AI tools in a development context—for debugging, workflow acceleration, rapid prototyping, and improving engineering efficiency. ________________________________________ Key Responsibilities: NLP & Machine Learning Engineering: • Build advanced NLP models for sentence parsing, context detection, semantic analysis, entity extraction, and policy language interpretation. • Develop hybrid ML + rule-based systems that support governance modeling and policy decomposition. • Create pipelines for text ingestion, annotation, categorization, and structured language extraction. • Design evaluation frameworks for accuracy, drift, reliability, and linguistic precision. • Research and implement non-LLM NLP methods relevant to governance and policy analysis. Full-Stack Engineering: • Develop production-ready applications using Python (spaCy, NLTK, TensorFlow, or PyTorch to build and optimize NLP models), Django, JavaScript, HTML, CSS, and modern tooling. • Further develop NLP models for PoG™ Feature enhancements. • Develop and maintain secure, scalable REST APIs and backend services. • Integrate ML components seamlessly into PoG™’s architecture. Production Deployment & DevOps: • Dockerize machine learning pipelines and full-stack applications for uniform deployment. • Deploy and manage services in cloud production environments (AWS, GCP, or Azure). • Set up CI/CD pipelines, monitoring, observability, and scalable containerized processes. • Ensure production performance, uptime, and system reliability. AI Automation for Engineering Efficiency: • Use agentic AI tools to assist with debugging, test generation, workload orchestration, and internal development workflows. • Integrate AI-assisted coding tools responsibly into engineering processes. Contribute to the Proof of Governance® Platform: • Build NLP and ML components that strengthen PoG™’s ability to: • Map policy language into structured governance data • Detect enforceability gaps • Identify policy dependencies and contextual interactions • Deliver measurable, enforceable governance intelligence • Collaborate with PoG™ architects to extend platform intelligence across governance domains. ________________________________________ Qualifications: Required Skills & Experience: • 6–10+ years of software engineering experience with specialization in ML and NLP. • Mastery of sentence parsing, syntax/semantic analysis, dependency modeling, and contextual extraction. • Proven experience commercializing enterprise AI or ML-driven applications. • Proficiency in: o Python o Django o JavaScript o HTML / CSS • Demonstrated ability to dockerize applications and deploy them into production. • Strong understanding of ML architecture, data modeling, distributed systems, and backend engineering. • Experience using agentic AI tools for engineering workflows (debugging, code analysis, test generation). • Strong cloud engineering experience (AWS, GCP, Azure). Preferred Qualifications: • Background in computational linguistics or structured policy analysis. • Experience with ontologies, taxonomies, or governance modeling. • Prior work in regulated, audit-heavy, or mission-critical environments. • Contributions to high-scale enterprise software platforms. ________________________________________ Who You Are: • You excel in both advanced NLP engineering and full-stack software development. • You can design systems end-to-end—from custom algorithms through front-end integration to production deployment. • You understand how to use AI to accelerate development processes. • You are driven by building systems that transform governance from assumption to measurable, enforceable proof. • You are excited to contribute to the continuous evolution of PoG™

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.

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.