AEI Initiatives is developing Core-E, a governance architecture for stability-preserving optimization in consumer systems — letting optimization work without destabilizing the people and systems it affects. Phase I is NSF-funded feasibility research applied to grocery systems. This is a U.S. based, remote position. The Role: Lead the technical execution of Phase I validation work: - Measuring real action-effect parameters against retail data - Analyzing closed-loop optimizer–governor dynamics - Characterizing the regime boundaries where governance nets positive versus where it fails Required Qualifications - Graduate degree (MS/PhD) in operations research, applied mathematics, control theory, statistics, econometrics, or a related quantitative field - Demonstrated experience with time-series modeling, stochastic systems, or parameter estimation from messy real-world data - Ability to reason about feedback loops — whether coupled dynamical systems converge, oscillate, or degrade - Experience with simulation, Monte Carlo validation, or sensitivity analysis - Production-quality, documentable Python -**Above all: intellectual honesty. You’ll run tests that may prove the hypothesis wrong, and we need someone who reports what the data actually says. Nice to Have (Not Required) - Grocery, retail, or supply-chain optimization experience - Familiarity with control-barrier functions, runtime assurance, or AI safety - Background in econometrics or causal inference Time & Terms - Approximately 0.25 FTE (~10 hours/week) - 6-month duration - Contingent on NSF Phase I award (expected notification Q3 2026) - Structurable as W-2 employee, independent contractor, or subaward depending on your situation What Makes You the Right Fit You’re the person who, when shown a model that doesn’t behave as expected, gets *more* curious, not defensive. You understand that “the data says no” is a publishable result. You think in systems, not just in metrics. And you’re willing to work on something genuinely uncertain because the uncertainty is the point. *To apply, contact Valentina by submitting a job proposal.

I am looking for an experienced AI engineer to fix one critical issue in an existing AI agent. The AI agent is already built and working correctly in most workflow sections. It is a document processing and structured data extraction agent. The agent receives uploaded documents, analyzes the content, extracts required fields, validates the extracted information, and generates a structured final output for the application. However, there is one critical issue in the validation and final-output generation section of the workflow. In some cases, the agent marks the process as completed and returns a final result even when required extracted fields are missing or the validation step has failed. This causes the application to treat incomplete or invalid document data as successfully processed. This is not a full rebuild. I only need an experienced engineer to investigate the existing workflow, identify the root cause, and fix this specific issue. Detailed technical documentation is attached. Please review it before applying. Timeline: Urgent / start as soon as possible Please apply only if you have experience AI agents, LLM workflows, tool/function calling, validation logic, and state management.

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.

The Role: As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Prism AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. Our mission is to make Prism AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Prism inference engine. Key Responsibilities: Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers Building frameworks and safeguards to ensure Prism AI has the best model quality in the industry Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions Participate in code reviews, technical discussions, and continuous integration and deployment processes Minimum Qualifications: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience) 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems Strong programming skills in Python, Go, or a similar language Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.). Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) Preferred Qualifications: 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems Experience with open source inference engine like vLLM, Sglang, or TRT-LLM Contributions to open-source infrastructure or ML projects Experience in building large scale ML/MLOps infrastructure

We’re looking for experienced AI professionals to provide short, original quotes, practical insights, and light content feedback for our educational articles and guides. Your real-world perspective will help make the content more accurate, useful, and trustworthy for readers. The initial project involves reviewing and contributing to one guide, with the possibility of ongoing work. Example guide: onlinemastersdegrees.org/best-programs/information-systems/ **What You’ll Do:** * Review AI education content for accuracy and clarity * Leave light feedback through Google Docs comments * Provide brief expert quotes, usually 2–5 sentences each * Offer practical insights based on real-world AI, machine learning, or data science experience * Help add context around AI careers, degree programs, certifications, skills, tools, and industry expectations **For the Initial Project:** We’re looking to add approximately 3–4 short expert quotes to one AI guide. Quotes should be original, practical, and based on your professional experience. **Details:** * $30 per page * Pages typically take 20–30 minutes * Clear guidelines and examples provided * Contract, flexible, and ongoing work **Relevant Experience May Include:** * Artificial intelligence * Machine learning * Data science * Generative AI * Natural language processing * Computer vision * AI product development * MLOps * AI governance, risk, or compliance * Responsible AI * AI education or workforce development **In your submission, please include:** 1. A few sentences about your AI background, professional experience, and areas of expertise 2. Any relevant degrees, certifications, credentials, or notable AI projects 3. Link to your LinkedIn profile To help us sort through automated submissions, please put the name of Shopify’s CEO at the top of your submission.

We have a small Python-based machine learning inference service built with FastAPI and scikit-learn. The model was trained on structured tabular data, but our prediction endpoint is currently failing because of feature mismatch errors between the training pipeline and incoming API payloads. We need an experienced ML/MLOps engineer to quickly debug the issue, clean up the preprocessing logic, and make the `/predict` endpoint work reliably again. The goal is not to retrain the full model or build a large system. We only need a focused fix: review the existing model artifact, inspect the expected feature columns, update the API preprocessing code, and provide a short explanation of what was wrong. Bonus if you can also add a simple test request example or basic validation for missing fields. This should be a quick one-time task for someone comfortable with Python, scikit-learn, Pandas, FastAPI, and ML deployment workflows.

I want to build a private multi-model RAG-based Opportunity Intelligence Agent. It should support document ingestion, opportunity-specific workspaces, vector search, source citations, multi-model routing across OpenAI, Claude, Perplexity, and possibly DeepSeek, and generate strategic recommendations from both uploaded files and live web research. This is intended to become a reusable base agent capable of knowledge retrieval, web research, multi-model orchestration, document analysis, citation generation, and agent clonding and configuration. It will be used for analyzing & strategy development for project opportunities, responding to RFPs, and proposal assistance, as well as other applications.

Looking for tutor for Anthropic's Claude AI. The tutor is fluent in English and proficient in Claude AI. Looking for one or twice a week of one hour tutoring.

Need an AWS Rekognition Custom Labels expert to improve an image classification model for identifying plumbing parts. Current model accuracy is approximately 55%. Dataset consists of approximately 300+ images per item captured with a Foldio turntable. Need assistance with: Dataset review Training strategy Classification vs object detection recommendations Improving model accuracy to 90%+ AWS Rekognition Custom Labels implementation Experience with computer vision and AWS Rekognition required. Deliverables: Review the existing dataset Create a new image capture strategy Train the model Test the model Document the entire process 2 hours of screen-sharing sessions explaining everything

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