I am seeking a highly qualified independent technical expert to review my professional work and, if appropriate based on your independent assessment, prepare an expert opinion letter for an employment-based U.S. immigration matter (EB-1A). The ideal candidate should have: Sr Faculty in reputed university, Ph.D. or equivalent senior industry expertise Oil and Gas, Computer Science, Artificial Intelligence, Cloud Computing, Data Engineering, or a related discipline. 15+ years of experience in the Oil & Gas or Energy industry. Recognized expertise in AI, machine learning, cloud architecture, digital transformation, industrial IoT, operational data platforms (e.g., AVEVA PI, OSDU), or enterprise data management. Strong technical writing skills and the ability to clearly explain complex engineering concepts. The engagement would involve: Reviewing my published articles, technical documentation, project summaries, and supporting materials. Independently evaluating the originality, technical significance, and industry impact of my work. Preparing an objective expert opinion letter describing your assessment of my contributions, their technical significance, and their relevance to the broader energy industry, if supported by your independent review. The letter should reflect your own professional judgment and be based solely on your independent evaluation of the materials provided. Please include with your proposal: A résumé or CV. A brief summary of your experience in AI, cloud technologies, digital transformation, or energy systems. Any prior experience serving as an expert witness, peer reviewer, technical evaluator, or preparing independent expert opinions (if applicable). Confidentiality will be required before project materials are shared.

## Project Overview I am seeking an experienced wearable biosensing hardware engineer to design, build, program, assemble, and test a fully working prototype of a compact wearable physiological monitoring device. The prototype must acquire synchronized data from multiple sensors, store and transmit the data reliably, and be suitable for use in future machine-learning development. This is not a request for a concept drawing, general consultation, breadboard demonstration, or component recommendation only. ***The final result must be a physically assembled and functioning prototype that can be worn and used to collect real-world data.*** The detailed application of the device will be disclosed to the selected engineer after execution of a confidentiality agreement. ## Primary Responsibilities The selected engineer will be responsible for the complete hardware-development process, including: 1. Reviewing the technical requirements and identifying any engineering risks or conflicts before beginning the design. 2. Selecting appropriate sensors, electronic components, electrodes, connectors, battery, charging system, microcontroller, memory, and wireless communication components. 3. Preparing a complete Bill of Materials, including manufacturer names, exact part numbers, quantities, estimated costs, suppliers, and lead times. 4. Designing the electronic architecture, schematic, PCB layout, power system, sensor interfaces, signal-conditioning circuitry, and communication system. 5. Developing the embedded firmware required to operate the sensors, synchronize the data, store the data, and transmit the data through Bluetooth Low Energy. 6. Building and assembling the working prototype. 7. Testing and validating each sensing channel individually and then testing the complete integrated system. 8. Providing all engineering files, source code, design documentation, and operating instructions at the completion of the project.

I need someone who has experience with AI Apps that are deals with construction estimating and a fast learner. Must be able to learn the App and teach it to another worker.

I am a solo inventor and systems architect seeking a high-level Technical Writer or Embedded Systems Engineer with deep expertise in Edge AI, Neural Processing Units (NPUs), and biometric digital signal processing (DSP).I have recently filed a comprehensive, 16-page provisional utility patent application (AS-2026-PROV-001) with 21 highly detailed system architectural figures. The platform focuses on hardware-agnostic, edge-native, multi-species emotional-AI and clinical biometric tracking using micro-ballistocardiography (BCG), 200Hz kinematic tracking, and INT8 quantization loops on ARM Cortex micro-architectures.I am currently managing active, inbound M&A evaluation cycles with tier-one global veterinary diagnostic conglomerates and market-leading consumer pet wearable brands. To move to the deep legal due diligence phase under NDA, I need to package the patent text and drawings into a formal, corporate-grade "Technical Integration Guide & Developer SDK Specification Blueprint."Your Core Responsibilities:Data Taxonomy Blueprints: Translate my patent parameters (Feline Purr-State Disambiguation at 20–45 Hz, 200 Hz micro-gesture kinematic tracking vectors, Equine Limb-Flight Asymmetry Index, etc.) into a highly structured mathematical metadata spreadsheet / data dictionary layout (Excel format) for corporate data science review.Algorithmic Flowcharts & Logic Outlines: Convert my 21 patent figures (specifically the raw signal capture, signal conditioning, adaptive noise filtering, and 7-Dimensional Emotion Vector generation pipeline) into clean, visually polished professional engineering flowcharts using Draw.io, Lucidchart, or Visio.INT8 Quantization Specifications: Author an authoritative, standard technical document defining the local edge inference rules, model compression parameters, and scheduler priorities that guarantee our sub-300ms local execution benchmark on generic NPU configurations.Requirements:Proven experience writing technical dossiers, SDK specifications, hardware reference design briefs, or developer documentation for enterprise clients.Strong background in machine learning quantization (INT8/FP16), edge-native model scheduling, embedded firmware architectures (ARM Cortex), and multi-sensor data fusion.Familiarity with clinical biometric telemetry (HRV epoch analysis, cardiac wave decoupling, respiratory cadence) is highly preferred.Must sign an absolute, ironclad Non-Disclosure Agreement (NDA) and IP Assignment verification prior to accessing the provisional patent specification.Project Type: One-time project with a potential hourly retainer to serve as an on-call "Technical Firmware Advisor" during upcoming corporate due diligence video calls.

ONEai Health is seeking an exceptional technology leader to help advance and scale our AI-driven healthcare platform. We are a category-creating health technology company transitioning from validated product-market fit into the execution and hypergrowth phase of the business. We are cash-flow positive, expanding commercially, and raising a significant funding round to accelerate technology development, advance our mission, and increase headcount. We are NOT entering an established category. WE ARE CREATING ONE. Our Continuous Care Intelligence Platform transforms patient-generated health data, reported outcomes, and clinical dialogue into predictive and suggestive intelligence that helps care teams detect risk earlier, intervene faster, and improve outcomes for people living with chronic disease. THE OPPORTUNITY We are looking for a highly experienced fractional CTO, interim CTO, or senior technology executive who can provide immediate US-based leadership to our high-performing offshore development teams. The teams are strong. The opportunity is to improve strategic alignment, communication, technical prioritization, execution discipline, architecture, AI development, and accountability as the company enters its next stage of growth. This is NOT a passive advisory role. We need a doer- Smart, amazing work ethic, high integrity, with grit and stamina, MUST be a problem solver--RESULTS DRIVEN. Someone who can quickly understand the platform, identify what matters most, create clarity, lead technical decision-making, and turn changing market and customer needs into executable development priorities. KEY RESPONSIBILITIES • Assess the current platform, architecture, AI capabilities, data infrastructure, development processes, security posture, and technical risks • Create a clear technology strategy and prioritized execution roadmap • Lead and coordinate offshore engineering and AI teams • Translate customer, clinical, regulatory, and commercial requirements into clear technical deliverables • Improve development velocity, quality, accountability, and delivery predictability • Advance AI, predictive analytics, machine learning, and large language model capabilities • Strengthen platform scalability, security, reliability, interoperability, and observability • Identify and develop defensible technology, data, workflow, and intellectual-property moats • Establish engineering metrics, operating cadences, architecture reviews, and delivery reporting • Help recruit and assess future engineering, AI, data, architecture, and security leaders • Communicate technology strategy, progress, risks, and investment needs to the executive team and board IDEAL BACKGROUND You may be a strong fit if you have: • Served as a CTO, fractional CTO, VP of Engineering, Chief Architect, Head of AI, or senior technology executive • Built or scaled an AI-driven enterprise software, digital health, healthcare technology, or data platform • Led distributed or offshore development teams • Moved meaningful AI or machine-learning capabilities from concept into production • Designed scalable, secure, cloud-based enterprise platforms • Experience with predictive models, generative AI, large language models, MLOps, and AI governance • Experience with healthcare data, FHIR, HL7, EHR integrations, patient-generated health data, medical devices, or regulated technology • Recruited and developed exceptional technical talent • Led architecture modernization, technical-debt reduction, or platform transformation • Presented effectively to founders, executives, boards, investors, enterprise customers, and technical teams WHAT WE VALUE We are looking for a leader with: • An ownership mentality • Entrepreneurial judgment • Deep technical credibility • A bias for action • Direct and precise communication • Strong execution discipline • High standards • The courage to make difficult decisions • The ability to solve problems systematically and permanently • A genuine desire to build people, not merely manage tasks THIS ENGAGEMENT IS NOT FOR YOU IF: • You prefer advisory work without accountability for execution • You speak about AI but have not deployed meaningful AI into production • You require perfect information before acting • You avoid difficult talent or performance decisions • You tolerate missed commitments or recurring quality problems • You create unnecessary process • You blame offshore teams, vendors, legacy code, or inherited architecture • You are satisfied with incremental improvement INITITIAL ENGAGEMENT The initial engagement will focus on: 1. Technology and organizational assessment 2. Architecture and AI roadmap 3. Offshore team leadership and operating cadence 4. Priority delivery planning 5. Technology-risk identification 6. Platform scalability and moat strategy 7. Executive technology reporting This may begin as a fractional or project-based engagement, with the potential to develop into a long-term executive role for the right person. HOW TO APPLY Please submit: • A brief summary of your relevant experience • Examples of platforms you have built or scaled • An AI capability you moved into production • Experience leading offshore or distributed development teams • A major technical transformation you led • Your availability and preferred engagement structure Do not simply send a résumé. Tell us what you have built, what changed because of your leadership, and what measurable results you produced. We are looking for proof of consequence.

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.

We are seeking a highly experienced Advanced Artificial Intelligence instructor and curriculum consultant to design, customize, and deliver a private, instructor-led AI training program for a group of six participants. The program is expected to begin in October 2026 and may be delivered onsite in San Francisco, San Diego, or Los Angeles, California. The final location and schedule will be determined with the selected instructor and client. Program Overview This engagement is intended for experienced AI practitioners who can connect advanced technical concepts with practical enterprise implementation. The selected instructor will work with the client and NobleProg to determine the appropriate program duration, sequence the curriculum, customize the content, prepare hands-on labs, and deliver the training. The program should provide participants with practical knowledge of modern AI engineering, enterprise AI architecture, AI governance, large language models, production AI systems, and the infrastructure required to deploy and manage advanced AI solutions. Requested Curriculum Areas The proposed program should address the following subjects: AI governance, Responsible AI, and AI risk management Large language models and foundation models LLM training, fine-tuning, and distributed training LLMOps and production AI MLOps and AI platform engineering On-premises AI, private AI, and data sovereignty Agentic AI and multi-agent systems Retrieval-Augmented Generation Knowledge graphs and enterprise knowledge integration AI infrastructure and GPU clusters Distributed computing for AI workloads High-performance model inference AI security and model security Regulatory compliance and enterprise AI controls Enterprise AI architecture and deployment strategy The final curriculum may be adjusted based on participant backgrounds, available infrastructure, program duration, and the client’s technical priorities. Training Requirements The training should preferably be delivered as a private, instructor-led program and include: Practical exercises and demonstrations Instructor-guided hands-on labs Realistic enterprise AI use cases Participant training materials Lab instructions and supporting resources Recommendations for continued learning Certificates of completion Opportunities for participant questions and technical discussion Instructor Responsibilities The selected instructor will be responsible for: Participating in a client alignment and discovery meeting Assessing the participants’ technical backgrounds and learning objectives Recommending an appropriate course duration and training schedule Developing a proposed course outline Customizing the curriculum for the client Identifying participant prerequisites Defining all hardware, software, cloud, and GPU requirements Preparing practical exercises and hands-on labs Delivering the training virtually or onsite Providing training materials and supporting resources Recommending certificate or continuing education options Coordinating with NobleProg throughout the engagement Program Details Anticipated start: October 2026 Number of participants: Six Potential onsite locations: San Francisco, California San Diego, California Los Angeles, California Delivery options: Private onsite instructor-led training Private virtual instructor-led training A blended or hybrid schedule, when appropriate Applicants should indicate which locations and delivery formats they can support. Ideal Instructor Qualifications The ideal instructor will have substantial professional experience in several of the following areas: Enterprise artificial intelligence Machine learning engineering Large language model development and deployment Foundation model training or fine-tuning Distributed model training LLMOps and MLOps AI platform engineering Agentic AI and multi-agent architecture Retrieval-Augmented Generation Knowledge graphs GPU infrastructure and AI clusters High-performance model serving and inference Private or on-premises AI deployments Data sovereignty and regulated data environments AI governance and Responsible AI AI security, compliance, and risk management Enterprise AI architecture Previous experience delivering advanced instructor-led training to corporate, government, engineering, or technical audiences is strongly preferred. Relevant certifications, publications, conference presentations, open-source contributions, enterprise projects, or academic credentials should be included in the application. Proposal Requirements Please provide the following information with your application: A summary of your relevant AI experience Your instructor-led training experience Your experience with enterprise or government clients Your recommended course duration Your proposed training schedule A preliminary course outline Recommended technical prerequisites for participants Required participant software and development environments Required cloud platforms, GPU resources, or local infrastructure Your ability to support virtual delivery The onsite locations you can support Your availability during October 2026 Relevant certifications and professional credentials Examples of similar training programs you have delivered Certificate or continuing education options you can support Any applicable corporate, government, or group-training considerations Confirmation that you accept the stated daily rate Please clearly identify which curriculum topics you can teach directly and whether any portions of the program would require an additional specialist. Compensation Training rate: $1,200 per day of training delivery The final project compensation will be determined by the approved number of training days. The agreed compensation is expected to cover: One client alignment and discovery meeting Curriculum planning Training material preparation Reasonable curriculum customization Hands-on lab preparation Instructor-led training delivery Reasonable local travel expenses, including gas, Uber, and parking, will be reimbursed when necessary and approved in advance. Any airfare, lodging, extended-distance transportation, specialized cloud environments, GPU resources, software licenses, or other significant expenses must be disclosed and approved before the engagement begins. Payment Terms Payment milestones and invoicing terms will be finalized before the engagement begins. Applicants should disclose any required deposits, cancellation terms, or other payment conditions in their proposals. Important Links Additional program and curriculum details: https://docs.google.com/document/d/1EZ4KRYCaZZYtg3JX4eSu82H3pmjCRxEguHCbbTD7yJ0/edit?usp=sharing NobleProg instructor process and SOP: https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Please review both links before submitting your application. How to Apply Begin your application with the phrase “Advanced Enterprise AI Instructor” to confirm that you have reviewed the complete posting. Please submit a focused proposal that explains how you would structure this program for six participants, including the number of days you recommend, the balance between lectures and labs, and the infrastructure required to complete the hands-on exercises successfully.

About the Role We are seeking an experienced Contract Paralegal to support an innovative AI development initiative. This short-term contract role is ideal for a detail-oriented legal professional who enjoys analyzing complex commercial agreements and working at the intersection of law and technology. Working alongside attorneys and data scientists, you will review a large volume of contracts, extract and organize key contractual language, and help create the foundational data used to train an AI model. Key Responsibilities: Review and analyze 700+ commercial contracts across multiple agreement types. Identify, extract, and annotate language related to designated contractual provisions (e.g., assignment, change of control, termination, indemnification, confidentiality, governing law, limitations of liability, and other specified clauses). Locate relevant language throughout entire agreements, including exhibits, schedules, amendments, and ancillary documents. Aggregate extracted provisions into structured templates or datasets for attorney review. Ensure consistency, accuracy, and completeness of extracted contractual language. Collaborate closely with contract attorneys to support legal analysis and quality assurance. Work alongside data scientists and project stakeholders to improve the quality of training data for AI models. Meet aggressive project timelines while maintaining exceptional attention to detail. Follow established annotation guidelines and documentation standards. Qualifications: 2–3 years of experience reviewing and analyzing commercial contracts as a paralegal or legal professional. Strong understanding of common commercial contract provisions and legal terminology. Exceptional attention to detail and organizational skills. Experience reviewing large volumes of legal documents efficiently and accurately. Ability to identify relevant language across multiple sections of a contract. Strong written communication and documentation skills. Proficiency with Microsoft Office, particularly Excel and Word. Ability to work independently in a fast-paced, deadline-driven environment. Preferred Prior experience supporting due diligence projects or M&A transactions. Experience annotating legal documents or working with structured legal datasets. Interest in legal technology, artificial intelligence, or machine learning applications. Experience collaborating with cross-functional teams, including technical stakeholders. Success in This Role Will Mean High-quality extraction and annotation of contractual provisions across a large contract population. Consistent application of project guidelines and taxonomy. Accurate organization of legal language for attorney review. Meaningful contribution to the creation of training data that will power an AI platform.

Building a machine learning platform for an insurance related product, with a focus on data pipelines, feature and label design, model development, deployment planning, monitoring, and business impact. Looking for an experienced MLOps or applied ML engineer to provide weekly mentorship through structured project check ins. The implementation will remain entirely my responsibility. Each meeting will focus on the current state of the project. I will provide detailed context on what has been completed, the decisions being considered, current blockers, and the next stage of work. The mentor will review that specific situation, challenge assumptions, identify gaps, and provide direct feedback based on how the issue would be handled in a real production environment. The goal is not general instruction. Feedback should be specific to the project, its architecture, data, constraints, and business use case. Meeting Structure: - Approximately 60 minutes per week - Review of progress since the previous meeting - Discussion of current technical and business decisions - Review of architecture, pipelines, model design, or deployment planning - Identification of risks, missing requirements, and unnecessary complexity - Clear recommendations and next steps for me to complete independently Scope of Guidance: - Business use case and ROI analysis - Data architecture, quality, lineage, and source contracts - Record grain, joins, and entity matching - Feature engineering and leakage prevention - Label and outcome design - Model evaluation and business metrics - Experiment and dataset versioning - Batch and real time deployment - Monitoring, drift, retraining, and rollback - Reliability, privacy, security, and governance Work Expectations: This is a meeting based mentorship role only. The mentor will not be expected to: - perform implementation work outside scheduled meetings - write or maintain the codebase - prepare separate reports or deliverables between meetings - manage the project - provide ongoing asynchronous support - take ownership of delivery Required Experience: - Professional experience building or operating production ML systems - Strong understanding of MLOps, data engineering, deployment, and monitoring - Experience reviewing real technical systems and making practical recommendations - Ability to explain tradeoffs clearly and give direct, specific feedback - Willingness to challenge weak decisions rather than provide generic advice Working Style: Direct communication and practical feedback are important. I will prepare the project context and questions before each meeting, complete the work independently afterward, and return with results for review. Initial Engagement: The engagement will begin with one paid consultation. The session will be used to review the project, discuss the expected mentorship style, and determine whether recurring weekly meetings are a good fit.

We are a market research firm with an internal development team that is very capable. We are looking for an individual or team to act in a consulting role as we explore development work in the synthetic data / digital twins space.