I am seeking an experienced ML engineer to provide insights on the design of a model I am planning to build. Your expertise in model design and architecture will be invaluable in helping me make informed decisions.

We are looking for an entry-level Software Engineer who is strong in computer science fundamentals and algorithms. You will work on real-world software problems. This role suits someone who enjoys bridging theory and practice: thinking carefully about problem formulation, writing clean and efficient code, and taking ownership of results end-to-end. ROLE OVERVIEW You will work within a small, cross-functional team to build software. You will be expected to think algorithmically, write quality code, and communicate your findings clearly to non-technical stakeholders. KEY RESPONSIBILITIES Analyse product and business requirements provided by the team. Select appropriate algorithms and architectures based on data characteristics, constraints, and performance requirements. Design and implement efficient data structures and algorithms. TOOLS & STACK Knowledge in these areas are preferred. Postgres and Mongo DB Machine learning and LLM frameworks Middleware and mobile concepts especially React-Native and Javascript/NodeJS Infrastructure: Basic familiarity with GCP or AWS Version control: Git QUALIFICATIONS Bachelor's degree in Computer Science is preferred Strong foundations in algorithms and data structures — able to reason about complexity and write efficient code. Good understanding of machine learning and LLM concepts Clear written and verbal communication; able to explain model behaviour and trade-offs to non-specialists.

Project Title: Build Fast Web-Based AI Anime Companion MVP (RAG + Merch Gen) – 15–30 Days, $25K Budget: $18,000–$25,000 fixed Timeline: 15–30 days (3 weeks preferred) Must-Have Skills: FlowiseAI (or LangChain/LlamaIndex), RAG pipelines, OpenAI/Claude/Grok, anime image generation (Leonardo.ai/Ideogram/PixAI), Vercel or Railway deployment Project Goal Build a mobile-friendly web AI Companion for Big A Anime that converts passive FAST viewers into active fans. The key objective is to connect our Pluto TV channel experience directly to the AI companion, allowing viewers to scan QR codes during live programming and instantly access interactive content, recommendations, and merch tied to what’s currently airing—no downloads required. Core MVP Features Web Chat Interface Clean anime-branded chat UI with voice input Mobile-first, responsive Custom domain (e.g., companion.biganime.tv) RAG Knowledge Base Ingest episodes, schedules, transcripts, and metadata Provide accurate recaps, lore, and “what’s on now/next” tied to Pluto TV programming Session memory + light user profiles AI Merch Generator Anime-style image generation (“me as [character]”) Leonardo.ai or similar integration Export + links to Printful/Printify FAST / TV Integration Tools Dynamic QR codes for on-screen use Deep linking between Pluto TV programming and companion experience Voice-friendly prompts (“Ask Big A Companion…”) Admin & Analytics Simple CMS for content uploads Dashboard: usage, queries, merch clicks Technical Requirements Global hosting (CDN) FlowiseAI preferred Full source + documentation 30 days post-launch support Out of Scope Native apps, payments, deep integrations, multi-language Deliverables Live URL, admin access, training, source code, 30-day support Application Fixed bid + 3-week plan 2–3 relevant project links Willingness for small paid test ($500–$800)

Are you an experienced web developer who loves building applications, enjoys mentoring, and is excited about leveraging AI to code faster? I am looking for a sharp, collaborative technical partner to work with me live over Zoom to build out various web applications. A core part of our workflow will involve utilizing Claude (and other AI tools) to brainstorm, scaffold, and accelerate our development process. Instead of working in isolation, you will be partnering with me in real-time to solve problems, review code, architect solutions, and push projects across the finish line. What You’ll Do Live Pair Programming: Join scheduled Zoom calls to actively write, debug, and review code together. AI Collaboration: Work alongside me to prompt, refine, and implement code generated by Claude to speed up the development lifecycle. Web Application Development: Help build, test, and deploy functional, clean web applications from scratch or improve existing codebases. Architectural Guidance: Offer advice on best practices, database design, and framework selection based on project needs. What I’m Looking For Strong Technical Foundations: Proficiency in modern web development frameworks and languages (e.g., JavaScript/TypeScript, React, Node.js, Python, or similar modern stacks). AI-Fluent: You don't just know how to code; you know how to use AI tools like Claude efficiently to debug, generate ideas, and optimize workflows. Excellent Communication & Patience: Since we will be working live on Zoom, you must be a clear communicator who enjoys explaining technical concepts and brainstorming out loud. Problem Solver: A knack for breaking down complex feature requests into manageable, step-by-step development tasks.

About the Role Assembly Software is a B2B SaaS company serving law firm customers and is actively expanding its internal AI capabilities. We are seeking a highly skilled AI contractor to serve as our embedded AI program lead — someone who can own and advance the design, implementation, and governance of AI tooling across the entire organization. This is a hands-on, strategic role. You will work directly with IT leadership and cross-functional teams to assess our current AI landscape, close gaps, and build a mature, secure, and operationally excellent AI program. We are a heavy Anthropic/Claude shop. Strong familiarity with Claude, the Anthropic API, and the Model Context Protocol (MCP) ecosystem is a significant advantage for this role. Core Responsibilities • Audit existing AI tool usage and identify overlaps, gaps, and shadow IT • Design and implement a company-wide AI governance framework • Lead MCP server setup, integration, and lifecycle management • Configure and manage Claude Teams/Enterprise deployments • Build and maintain an internal AI Skill Library for staff use • Define AI security policies and data access controls • Evaluate and recommend new AI tools and vendors • Establish prompt engineering standards and best practices • Connect AI tooling to internal business systems (Salesforce, M365, Asana, and others) • Support AI integrations with sensitive data sources including our data warehouse and CRM • Produce documentation, SOPs, and executive-ready reporting • Train internal staff and stakeholders on AI capabilities and safe usage Required Qualifications • Hands-on AI implementation experience in enterprise environments • Deep familiarity with large language model platforms, particularly Anthropic Claude and OpenAI • Proven experience building and managing MCP (Model Context Protocol) servers and integrations • Strong understanding of AI security — data exposure risks, access scoping, governance controls, and audit logging • Experience integrating AI tooling with business systems such as Salesforce, Microsoft 365, or similar platforms • Ability to author clear governance documentation, security policies, and executive-facing deliverables • Comfortable operating independently with minimal oversight while maintaining strong stakeholder communication Preferred Qualifications • Hands-on experience with the Anthropic Claude API, including system prompt design, tool use, and agentic workflows • Background in B2B SaaS, legal technology, or other regulated industries • Familiarity with SOC 2 compliance requirements as they relate to AI tooling and data access • Prior experience standing up internal AI assistants or Copilot-style tooling connected to live business data • Knowledge of data warehousing and secure query patterns for LLM-to-database integrations • Familiarity with CI/CD workflows and lightweight DevOps for deploying AI services

We’re hiring an extraordinary developer to own and grow our Base44 apps and sales products. around the future of AI discovery 1. Future of AI Discovery Core Demo – https://pull-discovery-core.base44.app/ You’ll evolve https://pull-discovery-core.base44.app/ into a beautiful, fluid, high‑performance, full-functional future of AI discovery demo following our advanced and sophisticated technical blueprint Integrate and orchestrate AI models incorporating LLM's, Search and World Models into a seamless experience with no visible seams between UX and intelligence. Own front‑end performance, responsiveness, and micro‑interactions—animations, transitions, and state changes should feel intentional and “alive,” not bolted on. Implement robust logging and analytics to understand how users explore, where they get stuck, and how the discovery engine can adapt dynamically. 2. Book Sales Engine – Six‑Channel Publishing System The second current Base44 project is a system that operationalizes our comprehensive sales plan across six channels. SEE THE COMPREHENSIVE BOOKSALES PLAN ATTACHMENT UNDERNEATH THIS POSTING You will: Translate a detailed multi‑channel publishing strategy (KDP optimization, physical bookstores via IngramSpark, other digital platforms, libraries, bulk institutional sales, and authority‑engine content marketing) into concrete workflows, tools, and dashboards. Build internal interfaces and automations to: Track metadata, pricing, and promotions across Amazon KDP and other platforms. Monitor campaigns across TikTok, Meta, LinkedIn, YouTube, newsletters, and partnerships. Surface KPIs like BSR, review velocity, ad spend, email growth, library adoptions, and bulk orders in a single, coherent view. Design light internal UIs that make it easy for non‑technical team members to update copy, add titles, trigger campaigns, and view performance without breaking anything. Implement robust, testable integrations between Base44, external APIs, and data sources to keep everything in sync as we scale from 8 to 22+ titles and beyond. Who You Are We’re not looking for a generic “full‑stack dev.” We’re looking for an unusual combination of visionary and doer: Creative technologist mindset – You think in systems and interfaces at the same time. You care deeply about how a product feels as well as how it works. Obsessed with execution – You’re disciplined, structured, and relentless about shipping. You break ambiguity into sprints, reduce complexity into tickets, and never let projects stall. Proactive owner – You don’t wait for instructions. You propose better ways to do things, flag risks early, and bring options—not problems—to every conversation. Strong product sense – You can balance ideal UX with realistic constraints and understand when to ship v1 vs. when to invest in polish. Comfortable with complexity – Multi‑channel distribution, layered data flows, and evolving requirements don’t scare you; they energize you. Ideal Skills & Experience You don’t need all of these, but you should recognize yourself in most: 5+ years building production web applications, ideally with a strong front‑end/UI focus. Deep experience with modern web stacks (React/Vue/Svelte or similar) and TypeScript, plus comfort with Node or comparable back‑end runtimes. Strong visual/UI instincts: experience collaborating with designers or owning design yourself for data‑rich interfaces and dashboards. Experience integrating AI/LLM APIs and retrieval systems into real products (RAG flows, multi‑step tool use, chat‑like interfaces, recommendation engines). Experience with analytics and experimentation: event tracking, funnel analysis, A/B testing. Familiarity with publishing, ecommerce, or multi‑channel marketing systems is a plus (KDP, IngramSpark, email platforms, ad platforms, analytics). Prior work in environments like Base44 or other low‑code/agentic platforms is a strong plus, but not required if you learn fast.

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 am working through a design agency on an application for their end client. I think the agency will need you to contract with them directly, but I will manage the project for them. I have scoped out the project already, and our plan is to internally perform a design phase with the client to produce a prototype with Lovable. There may be minor changes to scope after that design phase. The purpose of the app is to create bespoke wedding gown concept images for potential customers of an online wedding dress store. I have provided the details below and attached as a PDF 1. Customer opens an AI dress/gown design experience from a link in their separate e-commerce site. - This can be presented in its own page, we don't want a chat window to be present on any other page - This will be a chat-based interface built into the content area of the page, instead of a popup - The design must be elegant, and match the theme of the e-commerce site - The top navbar and footer don't need to be exactly recreated in this subdomain site, but should look similar enough to create a seamless experience - There will be no integration with the e-commerce site, we need to keep these web apps completely separate 2. The customer must sign up for an account and purchase one credit to begin the AI session - We will need to set up the subdomain site with its own payment processing system and login system - The payment integrations are Stripe to facilitate credit card and Apple pay, and a basic Paypal integration 3. The customers should be able to use a magic link to sign into their accounts, instead of having to remember a password - The account should automatically remember the browser to reduce friction for future access to the app 4. When an AI session begins, we will ask the customer a series of questions programmatically to prime the AI agent so that it can deliver better results - The questions will need to use conditional logic, such that the first question which determines one of 3 main conditional tracks: What type of gown are you looking for? Wedding Gown, Evening Gown, Cocktail Dress - If Wedding Gown is selected, the AI should suggest for the customer to take go to a bridal store and pictures of themselves in different dresses they like and upload the pictures, and describe what they do and don’t like about each dress. It can ask this in the freeform chat, since it may make the most sense to let them fill out the entire questionnaire to stay engaged, and we should reduce the costs of development for the questionnaire by omitting any unnecessary UI that the freeform chat can provide. - It may be best to always just prompt for them to upload the inspirational image at the beginning of the freeform chat so we can omit unnecessary programmatic UI, but in the case of the Wedding Gown it will specifically ask the customer to peform the above task. - We may have other specific questions to add to the questionnaire depending on what conditional track the customer chooses, though only the 3 main branches of conditional logic based on dress type will be required. - Examples of general questions it will need to ask are as follows: -- silhouette -- neckline -- sleeves -- fabric -- embellishments -- color -- train -- length -- closure -- lining -- structure -- inspiration -- event type -- I didn’t get the exact list of questions yet from my client that we should ask in the initial questionnaire. Let me know if you will need this information to accurately provide a price for the development of this application 5. We should not display a concept image after the programmatic questionnaire, the customer will be taken directly into the freeform chat from the questionnaire. - The AI agent may start with an overview of the selected choices from the questionnaire, then can generate concept images at its discretion. 6. The AI should guide the customer through a freeform conversation - The conversation should begin with the AI asking the customer to subjectively describe their dream dress 7. The AI should also make a suggestion near the beginning of the conversation for the customer to upload at least one inspirational photo, but photo upload is optional - If the customer uploads an initial inspiration image, the AI agent should not attempt to figure out body type, measurements, or any other information that we can gather programmatically. - It should treat the inspirational image the same way it would treat any image the customer uploads during the freeform chat, to reduce the cost of development as much as possible. 8. Customer can proceed with a freeform conversation description - The customer should have the option to type in a chat and to upload images - The purpose of the conversation is for the customer to describe the desired dress or gown in an open-ended way 9. AI generates one or more concept images based on the conversation, as soon as it can once it has enough information - The AI model we select should be very good at generating these types of images, this is probably the most important quality the AI model needs to have - The concept images should have the same quality as the final image 10. It is acceptable to generate the gown on a mannequin or a real human model, however the dress must be photorealistic, not a sketch or cartoonish rendering. 11. The concept images that the AI generates and the final image should portray the body type and skin color which the customer specifies - It is very important for us to render the image of the garment on the correct body type - ex. Hourglass, pear-shaped, thin, plus sized, etc. -- Specific body measurements do not need to be factored into the rendering of the body type, it just generally needs to be able to render the garment on different body types. - It is also very important for us to render the garment on a human model or mannequin which has the same or similar skin color as the customer inquiring -- This is important for the customer to judge the garment color and fabric type that will look best on them -- This is also important to make the app inclusive for people of all racial backgrounds who might use the app -- It may be best not to display the face, or if human models are used, to use pictures of models with different racial backgrounds, to avoid bizarre mismatches between facial characteristics and skin tone - The AI agent should ideally prioritize pictures of garments from our client’s website to use as inspiration when it generates renderings in the freeform conversation, along with the customer’s description of what they want. However this is not a hard requirement, so it could be eliminated from the requirements if it greatly increases devlopment effort. - The requirement for the quality of the images that are generated will be somewhat subjective and so we will need to budget time for our client to request revisions to this based on their review of the system. - We need to build the image generation part upfront to ensure the quality is acceptable before we spend time on other parts of the application. 12. AI asks whether the generated concept is generally what the customer wants - Customer can revise the concept conversationally 13. AI can regenerate or refine images after customer feedback 14. The tone of the conversation the AI has with the customer is important. - We will want it to speak like a friendly expert seamstress. - This requirement will be somewhat subjective and so we will need to budget time for our client to request revisions to this tone based on their review of the system. 16. We ideally want the agent (both chat and image generation) to have deep expertise about fabrics and these types of garments in general, so it can guide the user through prompts, and render the chosen fabrics correctly - I think freeform chat will be necessary for the customer to explain which fabrics should be used where on the garment, instead of gathering this informaton in the programmatic questionnaire - The customer will likely revise the fabric selections after they see the initial renderings of the garment - We would like to avoid the costs of training an AI for this, so ideally we should use commercially available AI models which have been trained for this purpose, instead of having to train our own model. Prompting the AI with this information might be a cost-effective way to teach it this expertise 17. There will be certain restrictions on what types of colors or fabrics can be used in the dress designs - So, the agent should know these restrictions when it has the freeform conversation with the customer. - For example, the store owner will not be able to produce dresses with neon colors, tie dye colors, etc. - Our client will articulate a list of restrictions for us before we begin the project. 18. AI should never display links to other websites, or suggest for the customer to navigate to other websites 19. This AI might not need to be trained specifically for this industry, but we should at least use prompting to direct it to gather this kind of information, and to give it some background about what each of these things mean, so it can describe them to the customer. We basically need to make it as knowledgable as possble while keeping costs low. 20. The AI system the system should remember their active conversation - Since the customer will be required to have an account to use the AI system we can use that to automatically save the AI conversation - The saved conversation should preserve all the information that the customer input since the beginning of the AI session - A customer can only have one active AI session at a time - The customer cannot resume an AI session that has been completed - We don't need to provide a way for the customer to see the details of completed AI sessions 21. AI should have a fallback/human-help option if the customer gets stuck or the AI fails. - The fallback should collect enough information for an admin to follow up manually, so it should present a form in order to ensure that all the necessary information gets collected - A message should be displayed above the form, or somewhere on the page, to inform the customer that the entire conversation will be sent along with the form submission, so they know that they do not have to type all the details of the AI conversation - The app must present a button outside of the chat prompts after 3 - 5 chat messages have been sent, so the customer knows they have the option to terminate the AI conversation and manually ask for help. - That button would display the form - We don't want to display the button before any conversation has happened because we don't want customers to skip the chat altogether. -- One of the business goals of this app is to allow custom inquiries without overwhelming the support staff - Site admins must have the ability to adjust how many messages the button will display after, so they can control this threshold after they observe the results of real conversations - After the button initially displays, it should remain present in the view so the customer can easily access it at any point in the conversation 22. The freeform chat must be limited to something like 50 to 75 messages, in order to avoid excessive charges from the 3rd party AI services - This threshold should be adjustable from an admin portal - If this threshold is reached during the conversation, then we should force the customer to use the fallback form from requirement #21 to submit their inquiry 23. Customer can submit the completed design inquiry when satisfied. - During the submission process, the chat must ask for the following information, and present the following pre-written messages. This doesn't actually need to be executed by the AI model, but it can just be programmatically presented to the customer: - Ask for customer contact info, including name, email, and phone number. - Ask for requested event/date, while making clear the date is not guaranteed. - Ask for seamstress-relevant measurements, including bust, waist, hips, hollow-to-hem, shoulder width, bust point, underbust, waist-to-floor, arm length, bicep, wrist, back width, torso length, height, shoe height, and preferred fit. -- I still have to refine this list with the client, I am not sure if it needs to ask for all these things, or if there are some different things that I haven't listed here which it needs to ask for -- When it asks for this information it should display links under each measurement type to articles which describe how to produce each of the measurements. We can hardcode these links or allow the admin to specify each, they don't need to be generated by AI. - Prewritten disclaimer text should display. 24. The final submission should notify a list of email addresses set by a site admin. 25. The final submission will completely consume the credit used to purchase this AI session - The AI conversation cannot be resumed after the final submission - Another credit must be purchased to start a new AI conversation - New AI conversations will not have any memory of the previous conversations, any new AI conversations will start from a clean slate 26. Admins must have the ability to manually reset a credit, or assign a credit for free and cancel a current session, so the customer can start a new AI conversation. - This doesn't need to be very user friendly for the admin. If a session is reset this way, no knowledge of the previous conversation needs to be preserved. 27. Admins should be able to review partial, or completed conversations within a list in the admin portal - Each line item should display a status indicator to show if the conversation has been submitted yet, if an admin has began the review process, or if the item has been handled: Ex. In Progress, Submitted, In Review, Awaiting Payment, Handling, Ready To Ship, Closed - Admin should be able to see the answers to the programmatic questionnaire - Admin should be able to review the full conversation history - Admin should be able to review all uploaded photos/files - Admin should be able to review all AI-generated images, and the final one should be clear to them - Admin should be able to see the collected technical design details and measurements 28. Pricing of the garment remains manual and is handled by after review, the AI should not give any quote or present any pricing even if asked by the customer. 29. If the customer asks for pricing, the AI should display a prewritten script like this: "Pricing will be determined by the store owner after this conversation has been reviewed." 30. Invoices and payment will be handled manually through native WooCommerce custom order/invoice functionality which is already present in the e-commerce site, the AI system doesn't need to handle this at all. I mentioned this above on the requirements, but I want to reiterate since it is important and a hard requirement for how the development milestones must be structured: - The requirement for the quality of the images that are generated will be somewhat subjective and so we will need to budget time for our client to request revisions to this based on their review of the system. - We need to do the image generation part upfront to ensure the quality is acceptable before we spend time on other parts of the application As an optional add-on to the scope of this project, can you give a separate estimate to enhance the AI such that it understands which kinds of modifications will increase or decrease the cost of producing the gown, so it can guide the customer in case they are asking for very expensive things. - It shouldn’t give any specific price numbers, but should give the customer guidance if additions or alterations will significantly increase or decrease the cost of production. - This will be to prevent the customer from being surprised when the store owner manually follows up with them with the price of the garment they designed. This client did agree to adhere to a strict schedule to provide feedback after each round of development, given that we complete each round of development on the schedule we agreed to. - However, this client has deviated from agreed schedules multiple times in the past on other projects I did with them, so you should factor that into your timeline and cost estimations - We cannot increase the development cost mid-way through the project, however we can adjust the development timeline if the client deviates from the schedule In your proposal, please also include a quote or estimate for the cost of hosting and ongoing maintenance after the app has launched - Our client can pay for the hosting directly - We will need at least ongoing updates to patch security vulnerabilities and ensure uptime of the app and all its features which will be defined by the scope of this project - We don't need a 100% 24/7 uptime SLA, but basically just keeping everything up to date so it stays stable, and we'd need someone to respond to outages within 24 hours - Outage response can consist of simple rollbacks, if necessary, as long as all the chat session info is at least provided to the client as a CSV or similar, along with all graphic assets from any conversations, so they don't lose any data from an outage - I would set the expectation with my client that we would treat any future support or enhancement requests to be additionally charged for on an as-needed basis

I need help completing this project today. The project sits between data engineering and AI. I have source data that needs to be cleaned, structured, and prepared so it can support both analytics and RAG-style AI workflows. The goal is to create a reliable pipeline that takes raw data, normalizes it, preserves basic metadata/lineage, and outputs clean datasets that can be used for dashboards, vector indexing, or internal AI tools. The work may include: - Reviewing the current source data and structure - Cleaning and normalizing datasets - Designing or improving an ETL/ELT flow - Preparing AI-ready data for RAG or vector search - Adding basic validation checks - Organizing outputs for analytics use - Documenting the final workflow clearly Ideal freelancer has experience with: - Python and SQL - Data engineering / ETL pipelines - Databricks, Spark, or similar tools - RAG data preparation - Data cleaning, validation, and modeling - Cloud data storage such as S3, Postgres, or similar This is urgent and must be completed today. Please only apply if you are available immediately and can work quickly without a lot of hand-holding. When applying, please include: - Your relevant experience with AI-ready data pipelines or RAG data preparation - The tools you would use - Confirmation that you can complete this today - Your estimated timeline for delivery

I need an expert senior software engineer that can provide consulting services around implementation best practices of LLM's and AI into existing application workflows. i.e. leveraging AI to extract data from a document as part of an ingestion pipeline.