Northgate Real Estate Group is seeking an experienced legal data engineer, RAG developer, or legal technology specialist to build an internal research platform focused on bankruptcy real estate brokerage engagements. The platform will identify and retrieve retention applications, retention agreements, fee agreements, declarations, objections, and court orders involving Northgate and competing brokerage and advisory firms. It must search filings from 2010 to the present, beginning with the Southern and Eastern Districts of New York, then expanding nationwide. The system should: • Integrate with CourtListener/RECAP or Inforuptcy or PACER • Download and organize responsive docket documents • Extract text from PDFs and scanned filings • Identify the broker, court, judge, case, document type, and docket number • Extract fee structures and important contractual provisions • Compare terms requested in the agreement with terms ultimately approved by the court • Track objections, revisions, and provisions removed from final orders • Allow users to search, filter, compare, and export results • Use AI or RAG to answer questions with direct citations to the underlying documents Important provisions include minimum fees, commissions, buyer’s premiums, treatment of forfeited buyer deposits and premiums, credit-bid fees, refinancing fees, workout fees, expense reimbursement, retainers, tail periods, protected buyers, exclusivity, indemnification, fee sharing, escrow, survival clauses, and payment timing. The ideal candidate has experience with PACER, CourtListener, RECAP, Inforuptcy, bankruptcy dockets, document extraction, OCR, APIs, vector databases, RAG systems, and legal document analysis. We are not looking for a general AI consultant. We need someone who can design and build a reliable, production-quality legal research system. We are looking to build a long-term internal platform, not a one-time research project. Once the system is built and configured, it should operate largely autonomously, automatically identifying new bankruptcy cases, retrieving relevant filings, extracting and classifying documents, updating the database, and making the information immediately searchable. It should require minimal ongoing maintenance beyond occasional software updates or enhancements, allowing our team to focus on analyzing the results rather than manually collecting documents.

We are building a cross-organizational AI agent trust and identity layer for regulated financial transactions. Our stack includes LangGraph (orchestration), Supabase (database + RLS), Clerk (identity), and Cloudflare Workers (API gateway). We need a Principal-level architect to conduct a one-time, 3-hour architecture validation session to stress-test our design BEFORE we write production code. THIS IS NOT A DEVELOPMENT JOB. This is an advisory/review engagement only. WHAT YOU REVIEW: - Agent identity model (registration, verification, scoping, revocation, cryptographic attestation) - Trust token protocol (issuance, verification, revocation, replay attack vectors, network partition behavior) - Compliance checkpoint architecture (GLBA/RESPA/E-SIGN rule placement, audit trail immutability, human approval gates) - Five-layer system architecture (Cloudflare → LangGraph → Supabase → Clerk → external integrations) WHAT WE PROVIDE 48 HOURS BEFORE THE SESSION: - Full architecture diagram and documentation - Pre-flagged findings from our AI agents (Compliance, Security, Architecture agents have already reviewed the docs — you validate their findings and catch what they missed) DELIVERABLE: - Written architecture review (2-4 pages) with: - Approved decisions (green) - Required changes (red — must fix before building) - Recommended changes (yellow) - Open risks - Explicit go/no-go on proceeding to build phase REQUIREMENTS: - Must have designed and shipped a production identity, auth, or trust system (not just configured one) - Experience with token-based auth (OAuth, JWT, mTLS) - Understanding of zero-trust architecture and agent identity - Ability to think adversarially (attack vectors, bypass scenarios, failure modes) - NDA required before any technical documentation is shared BUDGET: - Flat fee for the full engagement (pre-read + 2-hour session + written deliverable) - Please quote your rate in your proposal IF THIS SESSION GOES WELL: - We have 4 additional review sessions over the next 6 months (identity layer code review, trust token implementation, orchestration + API review, pre-pilot pentest readiness) TO APPLY: - Confirm you have shipped a production identity or trust system (name the company/project if possible) - State your flat fee for this engagement - State your availability for a 15-minute intro call this week - Include any relevant architecture review samples or past work Do NOT apply if you are an integrator, administrator, or generalist developer. We need someone who has ARCHITECTED identity systems from scratch.

Background - manufacturing engineering and AI company We're building a curriculum teaching the first-principles of AI in manufacturing and engineering automation to a mix of existing plant operators and software engineers with EE/ME backgrounds You should love reducing complex systems to their essential components - we're open to people from a range of backgrounds: AI systems, MES engineering, robotics automation, CNC integration

NobleProg is seeking an experienced AI Trainer to deliver a live, instructor-led workshop for a corporate client in the Irvine, California area. This four-hour workshop is designed primarily for the client’s Quality team, with invited participants from Pharmaceutical Sciences, Innovation Labs, and Clinical. The trainer should have strong expertise in enterprise generative AI and practical experience applying AI within pharmaceutical and life sciences organizations. The objective is to help AI Champions and business users identify meaningful opportunities to expand the responsible use of AI across regulated business functions. Engagement Details Location: Irvine, CA Duration: 4-hour workshop (9:00 AM – 1:00 PM) Audience: Quality team, with invited participants from Pharmaceutical Sciences, Innovation Labs, and Clinical Participants: Approximately 25 Format: Onsite + Remote Participants Rate: $750 Course Scope This interactive workshop introduces practical applications of generative AI across pharmaceutical quality, product development, clinical, and innovation functions. Participants will learn prompt engineering techniques, responsible AI practices, and practical business use cases while working within Microsoft Copilot and Enterprise ChatGPT. All exercises will use fictional or sanitized examples appropriate for a regulated pharmaceutical environment. NobleProg SOP: https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Key Topics Include * Practical applications of generative AI in pharmaceutical product development, including research support, summarizing background information, comparing options, and planning early-stage activities * AI-assisted clinical patient screening using fictional scenarios to structure eligibility criteria, summarize screening considerations, and develop review checklists while maintaining human oversight * AI for quality and compliance workflows, including drafting review checklists, summarizing procedures, identifying documentation gaps, and improving internal quality documentation * Cross-department AI use cases supporting Pharmaceutical Sciences, Innovation Labs, Clinical, and Quality teams through meeting preparation, project planning, reporting, knowledge management, and process improvement * Prompt engineering techniques for producing reliable, structured, and business-ready outputs * Responsible AI practices, data privacy, governance, and compliance considerations in pharmaceutical environments * Strategies for AI Champions to identify, evaluate, and promote practical AI opportunities across their teams Trainer Responsibilities * Deliver an engaging instructor-led workshop for approximately 25 participants * Facilitate hands-on exercises using Microsoft Copilot and Enterprise ChatGPT * Demonstrate practical AI use cases relevant to pharmaceutical quality, clinical, research, and innovation teams * Lead discussions on responsible AI adoption, governance, and human oversight * Coach participants on prompt development, prompt refinement, and evaluating AI-generated outputs * Encourage participants to identify AI opportunities within their own business functions * Provide training materials (trainer retains ownership of content) Required Qualifications * Strong hands-on experience using enterprise generative AI platforms, particularly Microsoft Copilot and ChatGPT * Demonstrated experience applying AI within pharmaceutical, biotechnology, healthcare, or life sciences organizations * Strong understanding of AI applications supporting Quality, Pharmaceutical Sciences, Clinical, Innovation, Regulatory, Manufacturing, or related pharmaceutical functions * Experience delivering instructor-led corporate AI workshops or executive education * Excellent presentation and facilitation skills with mixed-experience audiences How to Apply Please include: * A brief overview of your AI and pharmaceutical or life sciences experience * Your experience delivering corporate AI workshops or executive education * Examples of AI use cases you have implemented or taught for Quality, Clinical, Pharmaceutical Sciences, or Innovation teams * Confirmation of your availability to deliver onsite training near Irvine, California on Tuesday, September 8 * Any relevant presentations, publications, or previous AI training materials

Hello, I do not know if you can help us or even if I am looking in the right place. We have an inventory system called EPOS (which is from England). Their inventory system at the time was the only one that we could find to do what we needed done. They had a "developer" (in quotes because I'm not sure if that is what you call him) that wrote the code to talk to our EPOS system to our Shopify system. There was a lot of math being coded. We are quilt shop that sells fabric. The fabric is listed in EPOS by the inch. It is then converted to 1/4, 1/2 full yards, etc when a customer sees our listing in Shopify. I hope I haven't lost you yet. The coding also takes the cost from EPOS and converts it over to the cost in Shopify. A major glitch is causing the incorrect math conversion causing our fabric to show exponentially more price wise in our Shopify Store. Is this something you could look at or am I looking at the wrong type of professional perhaps. Thank you, Marla L.

Bellementis PLLC is seeking a contract legal automation specialist to help design and implement practical automation workflows for a fast-growing boutique law firm. The ideal candidate has experience with law firm operations, document automation, AI tools, workflow design, and systems such as Microsoft 365, Clio, NetDocuments, Gavel, Zapier, Notion, or similar platforms. Responsibilities may include: Automating client intake, engagement letters, matter opening, task tracking, billing workflows, and document generation Building reusable templates, checklists, and workflows Helping integrate AI tools into legal and administrative processes Improving knowledge management and internal firm operations Working with lawyers, operations, and technical team members to turn manual processes into scalable systems This is a contract role. Prior experience with legal tech, law firm operations, document automation, or AI-enabled workflow design is strongly preferred.

Engagement: Contract, phased. Phase 1 is a paid, standalone feasibility engagement with a go/no-go gate before Phases 2 and 3 are commissioned. Duration: Phase 1 approx. 1–2 weeks. Full scope approx. 1–3 months. Your compensation and evaluation are tied to the rigor of the method, not to what the data shows. Wherever the evidence is insufficient to support a claim, documenting that is a required and fully compensated deliverable. Our product architecture already assumes selective output: we display a result only where the evidence supports one and return "insufficient evidence" everywhere else. Both halves of that map are load-bearing, and we have no commercial preference for how the cohorts divide between them. We are not looking for a trading signal, an alpha model, or a predictive engine, and work that drifts in that direction is outside the scope of this engagement. What we want is a rigorous, classical event study: given this category of news event and this type of stock, what is the historical distribution of abnormal returns, with honest confidence intervals, and does it hold out of sample? We would rather fund a well-specified analysis whose answer surprises us than a poorly specified one that confirms anything. About Us We are a news curation and context firm serving US equity markets. We deliver curated, context-enriched headlines to over 200 institutions and more than 3,000 institutional traders on both the buy and sell side. We have been doing this for decades and are among the fastest on the street. Two features of our data matter for this work: Our archive is curated, not scraped. Roughly 2,000 items per day pass an editorial filter applied by former traders, PMs, and analysts. The same editorial logic produced our historical archive and our live feed, so there is no train/live mismatch in the sample definition. Timestamps are reliable. We are frequently first to print. Delivery timestamps closely track information arrival. Every headline in the archive carries an AI-generated sentiment label, a directional strength score, an accompanying model confidence score, and classification into up to 25 taxonomies. These labels are inputs to your analysis. Assessing their stability over time is in scope; generating new ones is not. We are building a retail-facing product on top of this archive. Your work establishes its empirical foundation. The Core Question For a given cohort (defined by some combination of stock characteristics, news taxonomy, and sentiment tier) what is the historical distribution of abnormal returns over six horizons following a headline, and how much of that distribution is statistically distinguishable from the cohort's unconditional baseline? The six horizons: Horizon Window 1 ~60 minutes post-headline 2 ~120 minutes post-headline 3 End of day (4:00 PM ET close) 4 Overnight / session gap 5 3 trading days 6 5 trading days Horizon definitions must be measured in trading time, not calendar time, with defined handling for headlines crossing outside regular session hours. Scope of Work Phase 1 : Methodology and Feasibility (paid, standalone, go/no-go) Deliverable: a written methodology document sufficient for a second statistician to reproduce your approach, plus a feasibility assessment against a data sample we provide. Must address: Abnormal return specification. Definition of the market/sector model used to isolate idiosyncratic return. Raw returns and own-volatility normalization alone are not sufficient. Volatility baselines. Time-of-day-conditional volatility estimation for intraday horizons. A trailing daily sigma scaled to one hour will not do. Cohort structure. How to group and condition. Our 25 taxonomies were built for editorial routing, not statistical pooling; some likely merge, some likely split. Our team will work with you on this as we know the market behavior and expect to inform the groupings. Thin-cell handling. Ticker-level cohorts will often have very few observations while pooled cohorts have many thousands. We expect a partial-pooling / hierarchical or empirical-Bayes approach, or a reasoned argument for a different treatment. Minimum evidence threshold. Derive our display threshold as a function of a stated minimum detectable effect and target power. We want the reasoning, not just a number. Dependence structure. Treatment of overlapping estimation windows, multiple headlines on the same name in the same window, and cross-sectional correlation on macro days. Baseline construction. Every cohort needs its own unconditional baseline. Absolute hit rates are not meaningful on their own. Validation design. How you will establish that estimates hold out of sample. Phase 2 : Estimation and Validation Full historical estimation across all valid cohort × taxonomy × sentiment × horizon permutations. Walk-forward out-of-sample validation. Freeze the estimates on an early period, run forward on data you have not touched, and report how many qualifying cohorts held up and by how much the effect decayed. This is a required deliverable and a primary measure of the project's success. Multiple-comparison control appropriate to the number of cells tested. Deliverable: a static reference matrix (CSV or Parquet) containing every cohort, its point estimate, confidence bounds, sample size, unconditional baseline, and qualification status across all six horizons. Deliverable: an explicit register of cohorts where no reliable effect was found. Phase 3 : Logic Transfer Documented mathematical logic and a reference implementation (Python) sufficient for our engineering team to reproduce the calculations in production. The normalization scheme converting validated estimates into a bounded 0–100 display score. Our current thinking is that this should derive from the lower confidence bound on the effect over baseline, so that sample size enters through the statistics rather than as a hand-set weight — we welcome a better proposal. Working session with our engineering team and written sign-off. -Data and Tools- All data is provided by us. No data sourcing, licensing, or acquisition is required on your part. Historical curated headline archive with AI-generated sentiment, strength, confidence, and taxonomy labels. Historical pricing data , which we supply: minute and 5-minute OHLC bars for intraday windows, daily aggregates for multi-day windows, and participant timestamp data covering pre-market (4:00 AM – 9:30 AM ET) and post-market (4:00 PM – 8:00 PM ET) sessions. Extraction volume and format can be adapted to your requirements. Tell us what you need and how you want it delivered. Language and libraries are your choice. Python or R both fine. Methodological Constraints All methods must be classical, auditable, and explainable line by line. This is a retail-facing product; every number we display must be defensible to a regulator, a journalist, or a skeptical user. No black-box or machine-learning models in the estimation pipeline. No neural networks, gradient boosting, or ensemble learners. No LLM-assisted analysis or interpretation. To be unambiguous: standard econometric and statistical tooling is expected and welcome. Hierarchical and random-effects models, empirical Bayes, shrinkage and regularization, bootstrapping, and power analysis are all classical methods and all in scope. The constraint is on opacity, not on sophistication. Explicitly Out of Scope Production software development, database design, or streaming pipeline work. Any form of signal optimization, strategy backtesting, or portfolio construction. Generating or revising sentiment/taxonomy labels. Product, UI, or commercial strategy. Who We're Looking For Strong fit: Formal training in econometrics, statistics, or empirical finance. Direct experience with event-study methodology — abnormal returns, estimation windows, cross-sectional aggregation, the Brown & Warner / MacKinlay / Kothari & Warner literature. Published or applied work measuring the price impact of information events. Comfortable delivering a null result and defending it. Willing to tell us when part of our framing is wrong. Our team is made up of former buy-side and sell-side traders, PMs, and analysts and we know the market, we do not claim to be statisticians, and we want to be pushed back on. Poor fit: Quantitative researchers whose instinct is to iterate until something profitable appears. Anyone who would treat "no effect found" as a problem to be engineered around. Generalist data scientists whose primary toolkit is ML. Anyone who would execute the methodology above without questioning any of it. Academic researchers, finance PhD candidates and postdocs, and practicing econometricians consulting on the side are all encouraged to apply. Screening Task (Paid) We will shortlist a small number of applicants and pay each for a 2–4 hour written task at their stated rate before any larger commitment. The task: You have approximately 5,000 news events across approximately 800 US equities. Propose a methodology for estimating the probability of a directional 3-day abnormal return, conditional on event category and stock characteristics, where per-ticker event counts range from 3 to 400. Address: how you define abnormal return; how you handle thin cells; how you set a minimum evidence threshold for reporting a result; and how you validate that estimates hold out of sample. Two pages. No code required. We are evaluating reasoning and judgment, not polish or length. To Apply Please include: A brief description of the most relevant event study or abnormal-return analysis you have conducted, including what the data showed and what you concluded. One example of a project where your conclusion was that the effect was not there. What you did, and how you presented it to whoever commissioned the work. Your view on how to handle cohorts with very few observations, in two or three sentences. Any published work or writing samples. Availability and rate. Applications that restate this posting back to us will not be reviewed. We would rather see one paragraph of real disagreement with our approach than three pages of agreement. An NDA will be required before data access.

### Job Description: AI Chatbot Developer We are excited to announce an opening for an experienced and innovative developer to join our dynamic team in the pursuit of creating an advanced AI Chatbot. This chatbot will be designed to perform essential business functions, including but not limited to lead generation, quoting, and providing exceptional customer support. Our ideal candidate will possess a robust background in AI technologies, particularly in the realm of chatbot development, and will be equipped with outstanding problem-solving skills that enable them to tackle complex challenges with creativity and efficiency. As a key member of our development team, you will collaborate closely with various departments to gain a comprehensive understanding of our specific operational needs and requirements. Your ability to translate these needs into a functional and user-friendly chatbot solution will be critical to enhancing our overall operational efficiency. We are looking for someone who is not just technically proficient but also possesses a keen sense of business acumen to ensure that the chatbot aligns with our strategic goals. In this role, you will be responsible for various aspects of the chatbot development lifecycle, including but not limited to: - Designing and developing the conversation flow and user interface of the chatbot, ensuring it is intuitive and engaging for users. - Implementing natural language processing (NLP) capabilities to enable the chatbot to understand and respond to user inquiries accurately. - Integrating the chatbot with existing systems and databases to facilitate seamless access to information necessary for lead generation, quoting, and customer support functions. - Conducting rigorous testing and quality assurance to ensure the chatbot performs reliably and meets user expectations. - Analyzing user interactions and feedback to continuously improve the chatbot's performance and expand its capabilities over time. - Staying current with the latest advancements in AI technologies and chatbot development to incorporate best practices and innovative solutions. You will also play a crucial role in training team members on how to utilize the chatbot effectively and will be expected to provide ongoing support and maintenance to ensure the chatbot remains up-to-date and functional. If you have a passion for artificial intelligence, a deep understanding of customer engagement strategies, and a desire to make a significant impact within our organization, we would love to hear from you! Join us in revolutionizing the way we interact with our customers and streamline our business processes through cutting-edge technology. This is a fantastic opportunity for someone looking to advance their career in a fast-paced, forward-thinking environment. Apply today and be part of our exciting journey towards enhancing our customer experience through AI!

We are a technology company that specializes in assisting organizations get the most of AI. This includes training and AI Agenet Development. We are looking for someone who can develop agents. We are a Microsoft partner and lead with Copilot but work with other AI engines upon the client's request.

We are seeking an experienced developer to build AI platforms that automate internal processes. The ideal candidate will have a strong background in AI development and a proven track record of delivering successful automation projects. Responsibilities include designing and implementing AI solutions, integrating with existing systems, and optimizing processes for efficiency. If you have a passion for AI and automation, we would love to hear from you!