I am seeking a highly analytical AI Prompt Engineer & Knowledge Strategist to enhance our AI system's understanding of education civil rights. The role involves crafting precise prompts and developing knowledge strategies to ensure the AI's accuracy and relevance. An NDA is required due to the sensitive nature of the topic. The ideal candidate will have a strong background in AI engineering and a keen interest in education and civil rights.

Looking for an elite problem solver, either an ex-MBB consultant with deep technical fluency or a seasoned AI Product Manager, to act as the crucial bridge between business operations and technical AI implementation. You will be responsible for dissecting client operations, prototyping the AI logic, writing flawless engineering briefs, and driving the development team to the finish line. What You Will Do: Workflow Audits & Discovery: Lead deep-dive discovery sessions. Map out existing operational workflows, identify bottlenecks, and pinpoint high-ROI opportunities for AI automation. Prototyping & Technical Translation: Because you are an AI power user, you will prototype the initial AI prompts and logic. You will then translate your operational findings into crystal-clear engineering briefs (or PRDs) so developers know exactly what to build. Development Management: Act as the project lead during execution. Manage the engineering workstream, clear roadblocks, and ensure the final solution is delivered on time, within budget, and achieves the strategic goal. Stakeholder Alignment: Act as the primary liaison between non-technical business leaders and the technical development team. Requirements & Qualifications: Top-Tier Pedigree: 1+ years of experience at a top management consulting firm (MBB, Big 4, Tier 2) OR proven experience as a Technical/AI Product Manager. Advanced AI Fluency: You are an AI power user. You possess advanced prompt engineering skills (e.g., chain-of-thought, few-shot) and know how to force LLMs to output reliable, structured data. Elite Structured Thinking: You excel at turning highly ambiguous, messy business processes into clean, logical frameworks (using Lucidchart, Miro, etc.) and comprehensive technical requirements. Project Leadership: Proven track record of managing technical resources, tracking deliverables, managing budgets, and driving teams to a deadline.

We are looking for a hands-on Forward Deployed AI Engineer to help build practical AI systems This is not a pure backend role and not a strategy-only consulting role. You will work close to end users, understand how their workflows actually operate, and then build AI-enabled tools that solve specific business problems. The ideal person is a strong software engineer who is comfortable with ambiguity, can communicate clearly with non-technical stakeholders, and can take an AI prototype from idea to something reliable and usable. What you will do - Learn the business workflows, systems, data, and constraints. - Build AI applications using Claude or similar large language models. - Use the right mix of prompting, retrieval, tool use, agents, and workflow automation. - Own delivery from scoping through prototype, testing, hardening, and handoff. - Create evaluations to determine whether the system is accurate, reliable, and safe enough to use. - Translate between domain experts and technical implementation. - Work carefully with sensitive or regulated data. - Document what you build so it can be maintained and reused. What we are looking for - Strong Python engineering skills. - Hands-on experience building with LLMs, preferably Claude or the Anthropic API. - Experience with RAG, structured prompting, tool use, evaluation, or agentic workflows. - Ability to operate independently in a messy, ambiguous environment. - Strong communication skills with both technical and non-technical stakeholders. - Track record of shipping working software, not just demos. - Comfort working with real-world data, integrations, and imperfect requirements. Helpful but not required - Prior forward deployed engineering, solutions engineering, or technical consulting experience. - Experience building AI tools for enterprise customers. - Experience in regulated or sensitive-data environments. - Familiarity with validation, auditability, traceability, or compliance-oriented workflows.

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 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.

I am a Ph.D. and digital product business owner who uses AI (Claude, ChatGPT, and other AI tools) every day to build, market, and scale my business. My 12-year-old son and I are looking for an experienced AI tutor who can teach us how to work with AI effectively—not just how to ask questions, but how to think, build, create, and solve problems with AI. This is an ongoing coaching relationship, not a one-time class. I already use AI daily and want to become significantly more advanced in prompt engineering, AI workflows, automation, and business applications. My son is curious, creative, and highly motivated. We want someone who can grow with him over the coming years as AI continues to evolve. WHAT WE ARE LOOKING FOR • Weekly one-on-one coaching sessions (one for me, one for my son) • Hands-on learning using real projects—not lectures or slide presentations • Practical skills that can be used immediately • A structured curriculum that builds over time • Someone who enjoys teaching and can explain complex ideas clearly • Experience with Claude, ChatGPT, and current AI tools MY LEARNING GOALS I use AI every day and want to continue improving how I work with it. Topics include: • Advanced prompt engineering • AI workflow design • Prompt refinement and iteration • Research and fact-checking • Marketing copy • Product descriptions • Sales pages • Email sequences • Business automation • AI-assisted content creation • Website content • Productivity systems • Emerging AI tools and best practices JORDAN'S LEARNING GOALS Jordan is 12 years old. While we'll certainly use AI for school projects and writing, our larger goal is to help him develop future-ready skills that will grow with him through middle school, high school, college, and beyond. We are looking for someone who can progressively teach him how to use AI to create, build, and solve problems. Topics may include: • Learning how to communicate effectively with AI and using AI to support academic success • Critical thinking and verifying AI responses • Research and creative writing • Brainstorming and problem solving • Website design and development with AI • Creating simple games with AI • Building apps and digital tools as his skills grow • Learning basic programming concepts using AI as a coach • Entrepreneurship and business ideas • Using AI to help businesses become more efficient • Marketing and content creation • Responsible and ethical use of AI • Developing confidence as a creator—not just a consumer—of AI technology The ideal tutor enjoys helping young people build real-world skills and can gradually increase the difficulty as Jordan grows. WHAT WE ARE LOOKING FOR IN YOU • Demonstrated experience teaching AI—not simply using it • Strong prompt engineering knowledge • Comfortable teaching both an adult professional and a motivated 12-year-old • Patient, engaging, and adaptable • Able to build a long-term curriculum instead of isolated lessons • Reliable, organized, and an excellent communicator Bonus experience: • Programming or software development • Website development • AI-assisted coding • Game development • Digital marketing • Entrepreneurship • Small business consulting LOGISTICS • Two weekly sessions (one for Jordan and one for me--45–60 minutes each) • Zoom • Weekly to start • Start date: ASAP • Budget: Please include your hourly rate. TO APPLY Please include: Your hourly rate. Your experience teaching AI and prompt engineering. An example of how you would structure Jordan's first month of lessons. An example of how you would structure my first month of lessons. What you think will be the most valuable AI skills for a motivated 12-year-old to develop over the next five years. Applications that do not answer these questions will not be considered. We are looking for someone who enjoys teaching, stays current with AI, and is excited about helping both a business owner and a young learner become confident, capable AI users and creators.

AI Developer Needed – Build Us a Marketing AI Agent We need a skilled developer to build an AI-powered Marketing Assistant for our business. **Core Tasks the Agent Will Handle:** - Appointment setting & lead qualification - Copywriting (emails, ads, social content) - Automated follow-up sequences - Lead research and CRM updates **Requirements:** - Experience with AI agent frameworks (LangChain, CrewAI, AutoGen, etc.) - Strong prompt engineering skills - Ability to integrate with our existing tools (CRM, calendar, email) - Past projects to show us – links or demos preferred **Budget:** Open to discussion based on scope **Timeline:** Looking to kick off within 1–2 weeks

Project Overview We are looking for an experienced AI workflow, process design, and prompt engineering expert to help us automate part of our sales and proposal development process. Currently, our project management team spends 4–12 hours developing a custom research plan and proposal for each active FSI lead. In busy weeks, we may work on 5–6 leads, which creates a significant time burden and slows down response time. We want to build a custom ChatGPT skill or AI workflow that can take sales notes, email context, and call notes, then help generate a research plan and proposal in our existing format. What We Need We need someone who can: Learn and map our current sales/proposal process Translate that process into a structured AI workflow Write effective prompts and decision trees Train or configure a custom ChatGPT skill/workflow Help the AI ask the right follow-up questions Generate proposal sections based on uploaded notes Recommend research scope, segmentation, targets, and options Output the final proposal in our existing template Desired Workflow The ideal AI workflow would allow us to upload notes from emails and sales calls. The AI would then ask a series of structured questions to determine how to write each section of the proposal. The AI should be able to: Recommend the appropriate research process Suggest project scope Identify demand segmentation opportunities Create tables for the proposal Recommend constituencies and companies to target Suggest research options Draft the proposal using our template Provide a strong first draft that our team can review and adjust Business Goal The goal is to significantly reduce the time spent developing research plans and proposals, especially for early-stage leads and marketing-generated opportunities. This is particularly important for new leads from companies we have not worked with before, where the probability of closing may be relatively low. We want to respond quickly and professionally without taking excessive time away from active client projects. Ideal Freelancer You should have experience with some or all of the following: AI workflow design Prompt engineering Custom GPTs or ChatGPT skills Sales/proposal automation Business process documentation B2B research or consulting workflows Template-based document generation AI-assisted decision trees Knowledge management or internal AI tools Experience with market research, consulting, or proposal development is a plus. Deliverables We expect the freelancer to deliver: A documented AI workflow/process map A set of structured prompts and instructions A functioning custom GPT, ChatGPT skill, or equivalent AI workflow Question logic for gathering missing proposal inputs Proposal section drafting logic Testing and refinement using sample lead notes Documentation so our team can maintain and improve the workflow Project Type This will likely begin as a one-time project, with potential for ongoing support as we refine the workflow and expand it to other proposal types. To Apply Please include: Examples of AI workflows, custom GPTs, or prompt systems you have built Your experience with proposal automation or business process automation Your recommended approach for this project Any questions you would need answered before starting

Senior Software Engineer (AI-Focused, Contract – US) Position Summary W Energy is seeking a Senior Software Engineer (Contract) to help drive the integration of AI capabilities into our core platform. This role is focused on building AI-powered product features, not just experimenting with models—embedding intelligence directly into workflows across our upstream and midstream solutions. You’ll design and implement AI-driven functionality that improves automation and user experience. This includes leveraging LLMs, machine learning models, and modern AI tooling within a production SaaS environment. This is a hands-on role for someone who can move quickly, make pragmatic decisions, and bring AI concepts into real, scalable product features. Responsibilities • Design and implement AI-powered features within the platform (e.g., automation, recommendations, copilots) • Integrate LLMs and/or ML models into existing services and workflows • Evaluate, select, and optimize AI tools, APIs, and frameworks for production use • Collaborate with Product to translate business problems into AI-driven solutions • Build and maintain scalable backend services to support AI functionality • Profile, test, and optimize performance of AI-integrated systems • Ensure reliability, security, and cost-efficiency of AI components in production • Contribute to architecture decisions around AI integration and system design • Partner with engineering teams to embed AI into existing applications without degrading stability Requirements • 5+ years of experience as a software engineer in a SaaS or cloud-based environment • Strong backend engineering experience (RoR and/or Golang preferred) • Experience integrating APIs and working within distributed systems • Hands-on experience with AI/ML tools (e.g., OpenAI, Anthropic, Hugging Face, or similar) • Experience building or integrating AI-powered features into applications (not just experimentation) • Strong understanding of data flow, system design, and performance optimization • Experience with relational databases (SQL Server or similar) • Familiarity with microservices architecture, Kubernetes, and CI/CD pipelines • Experience deploying applications in Azure or similar cloud environments • Strong problem-solving skills with ability to work in ambiguous, fast-moving environments • Builder mindset—someone who can take an idea and turn it into a working feature quickly • Pragmatic approach to AI (focus on value, not hype) • Ability to work independently in a contract environment while collaborating closely with internal teams • Strong communication skills and ability to explain AI concepts to non-technical stakeholders Preferred • Experience with prompt engineering, embeddings, or retrieval-augmented generation (RAG) • Exposure to model evaluation, fine-tuning, or AI performance monitoring • Experience with event-driven architectures or real-time data processing • Background in energy, fintech, or other complex data-driven industries

Seeking an experienced AI Solutions Architect to provide consultation and design an enterprise-grade Agentic AI platform capable of automating business workflows, retrieving knowledge from enterprise data sources, and integrating with existing business systems. This engagement focuses on solution architecture, technical design, technology selection, implementation planning, and best practices rather than full-scale application development. Scope of Consultation The consultant will: Assess current business processes and identify high-value AI automation opportunities. Design an enterprise Agentic AI architecture aligned with business and technical requirements. Define multi-agent workflows, agent responsibilities, and orchestration strategies. Design a scalable Retrieval-Augmented Generation (RAG) architecture for enterprise knowledge retrieval. Recommend the appropriate Large Language Models (OpenAI, Claude, Gemini, AWS Bedrock, Azure OpenAI, etc.) based on cost, performance, and use cases. Recommend vector database technologies and semantic search architecture. Design secure integrations with enterprise applications, APIs, and internal knowledge repositories. Define prompt engineering strategies, AI guardrails, evaluation methodology, and governance practices. Recommend cloud architecture and deployment strategies for AWS, Azure, or Google Cloud Platform. Provide guidance on LLMOps, monitoring, observability, security, model lifecycle management, and scalability. Develop an implementation roadmap, including phases, estimated effort, risks, and technical recommendations. Required Expertise Enterprise AI Solution Architecture Agentic AI Multi-Agent Systems Retrieval-Augmented Generation (RAG) Large Language Models (LLMs) LangChain LangGraph Prompt Engineering OpenAI API Anthropic Claude Google Gemini AWS Bedrock Python Vector Databases Enterprise System Integration AWS Microsoft Azure Google Cloud Platform AI Governance LLMOps MLOps Workflow Automation Deliverables Enterprise Agentic AI solution architecture document Multi-agent workflow design and orchestration diagrams RAG architecture and knowledge management design Vector database recommendation and data flow architecture Cloud deployment architecture Integration strategy for enterprise systems AI governance, security, and LLMOps recommendations Implementation roadmap with milestones and estimated effort Architecture review presentation and knowledge transfer session Project Outcome Delivered a comprehensive enterprise AI architecture and implementation strategy that provides a scalable foundation for deploying Agentic AI solutions. The consultation enabled stakeholders to make informed technology decisions, reduce implementation risks, accelerate development, and establish best practices for governance, security, and long-term operational success.