We are looking for a 3D GenAI Engineer / AI 3D Pipeline Developer It is necessary to create a solution that can qualitatively generate 3D models from a single image or multiple images. It is important that this is not just a ready-made demo, but a clear and reproducible process: from the input image to a full-fledged 3D asset with mesh, geometry, textures, and the possibility of further use. What needs to be done: - test modern image-to-3D models and approaches; - determine which option is best suited for our task; - use Trellis, Hunyuan3D, or similar solutions; - if necessary, use Gaussian Splatting in the 3D pipeline; - configure the conversion of Gaussian Splat / splat representation into 3D mesh; - obtain usable geometry; - generate high-quality textures; - bring the result to a usable 3D asset state; - find the optimal balance between quality, generation speed, and pipeline complexity; - build a clear process that can be repeated for different images; - perform fine-tuning, LoRA, or other model adaptations for specific types of objects.
About the Project We are looking for an experienced AI Automation Engineer to design and build a secure, self-hosted AI platform that combines a local Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and multiple AI agents to automate business workflows. This is a hands-on engineering role for someone who has experience building production AI systems—not simply integrating ChatGPT APIs. The goal is to create a private AI ecosystem capable of securely indexing company knowledge, answering questions using cited sources, processing meeting transcripts, and automating internal business processes. Responsibilities You will be responsible for: Designing and deploying a locally hosted LLM on a VPS or dedicated server Building a secure RAG pipeline using frameworks such as LlamaIndex or similar Creating document ingestion pipelines supporting PDF (including OCR), DOCX, TXT, XLSX and meeting transcripts Implementing document indexing, metadata management, deduplication, and versioning Developing AI agents for: Meeting transcription processing Automatic meeting summaries Action item extraction Client knowledge retrieval Building APIs or a simple web interface for querying the knowledge base Ensuring strict client data isolation and permission controls Implementing source-cited responses to minimize hallucinations Optimizing system performance, scalability, and reliability Writing documentation and deployment guides Performing testing and security validation Required Skills Strong Python development experience Experience with LLM frameworks RAG architecture experience LlamaIndex, LangChain, or equivalent Vector databases (Qdrant, Chroma, Pinecone, Weaviate, FAISS, etc.) Local/open-source LLM deployment (Llama, Mistral, Gemma, DeepSeek, etc.) API development (FastAPI preferred) Docker Linux server administration VPS deployment Git Authentication and access control Experience with OCR pipelines Experience working with structured and unstructured documents Fluent English What We’re Looking For The ideal candidate: Has built production AI systems from the ground up Understands RAG best practices Can work independently Thinks like a software architect—not only a developer Writes clean, maintainable code Communicates clearly Can recommend the best technologies instead of simply following instructions Project Type Freelance / Contract Remote Milestone-based Long-term opportunity for future AI automation projects Please Include With Your Application Portfolio of similar AI/RAG projects Examples of local LLM or AI agent implementations Estimated timeline Estimated project cost Hourly or fixed-rate pricing
There is a Telegram bot on aiogram/FastAPI (CRM for an event project) and a separate ManyChat bot in Instagram Direct for communication with clients.Task 1 — fix the logic of the Instagram Direct bot.The current bot is made according to a strict script: it works step by step (greeting → about the event → response to "too expensive" → response to "I'll think about it"), with 3 random text options for each step. The problem is that the bot does not understand the meaning of the message, but guesses the step number and sends a template that is off-topic. Because of this: when asked directly about the price, the bot does not provide numbers but sends general text the language jumps — sometimes Ukrainian, sometimes Russian essentially, it is a randomizer of templates, not a dialogueIn a week, the bot lost several live leads. I can provide screenshots of conversations as examples.Need: for the bot to actually analyze the content of the message (question about price, objection "too expensive", doubts "I'll think about it", non-standard question, etc.) and respond appropriately — based on AI (for example, Claude API), not according to a strict script. The tone should be lively, not robotic, and the language should be Ukrainian. Basic scenarios (price, discounts, handling "too expensive"/"I'll think about it") need to be preserved, but the AI should choose the appropriate response based on the context.Task 2 (optional, but also needed) — auto-posting in Instagram.Posting through the official Meta Graph API: reels, stories, posts, carousels. Tags in the bot: [reels] / [stories] / [post] / [carousel] + [geo] / [poll] / [countdown]. Multi-user panel /admin — adding/removing people, roles (posting / viewing / all), activity log. Dynamic price from Google Sheets — the bot pulls the current price from the "Price" column.Questions for the performer: Is it better to implement Task 1 (AI logic for Instagram Direct) on top of the current ManyChat via webhook + external AI processor, or as a separate bot on an architecture like aiogram/FastAPI, connected to Instagram through the official Meta API? Are you taking on both tasks together (Direct logic + auto-posting)? What is the total cost and timeframe for the entire scope (task 1 + task 2)?Requirements: Experience with Meta Graph API (Instagram) Experience integrating AI models (Claude API / OpenAI API) into chatbots Preferably — experience with aiogram/FastAPI or ManyChat webhooks Portfolio with similar projects
Project Title: Automation of content publication on social media via a Telegram bot using AI Task Description: Our company sells premium fitness equipment from the Italian manufacturer Panatta in Ukraine. We manage several social media platforms: Instagram, Facebook, TikTok, and a Telegram channel. An automated solution needs to be developed that will allow us to quickly adapt and publish video content across all our social media platforms. Main Logic of Operation: I send a link to a video from YouTube, Instagram, or TikTok to the Telegram bot. The system automatically retrieves the description of this video, translates it into Ukrainian, adapts the text to the live communication style of the Panatta brand, adds links to our social media, and publishes the finished post on: Instagram, Facebook, TikTok, Telegram channel. Important: The video itself does not need to be changed or translated. We need to work with the video description: translation, adaptation, text optimization, and publication together with the video or a link to it — depending on the technical capabilities of the specific platform. Desired Workflow Scenario: I send a link to the video to the Telegram bot. The system identifies the source: YouTube, Instagram, or TikTok. The system retrieves the original video description. AI translates the description into Ukrainian. AI adapts the text to the premium brand style of Panatta: professionally, lively, without dry machine translation. Links to our social media are automatically added to the text: Instagram, Facebook, TikTok, Telegram. The system prepares the publication for each platform. The publication is automatically posted on Facebook, Instagram, TikTok, and the Telegram channel. It is preferable to have the option to review the text and confirm publication via the Telegram bot before posting. Desired Functionality of the Telegram Bot: The bot should accept links to videos. The bot should display the generated Ukrainian text before publication. It is preferable to add buttons: "Publish", "Edit", "Generate Again", "Cancel". The bot should notify whether the post was successfully published on each social media platform. It is preferable to keep a history of processed links. Possible Technical Stack: We are considering various implementation options: Make.com, n8n, Zapier, custom backend, Telegram Bot API, OpenAI API, or other AI services. We are open to contractor suggestions regarding the optimal technical solution. Additional Considerations: It is necessary to check the technical limitations of the APIs for Instagram, Facebook, TikTok, and YouTube. A stable account authorization scheme needs to be proposed. It should be explained whether fully automatic publication is possible on all social media or if manual confirmation is required for some platforms. Protection against post duplication should be anticipated. The solution should be made as simple as possible for daily use. Expected Result: A working automation system where a link to a video can be sent via the Telegram bot, an adapted Ukrainian description can be received, and content can be published on Facebook, Instagram, TikTok, and the Telegram channel. What is required from the contractor in the response: Briefly describe how you propose to implement this solution. What stack will you use: Make.com, n8n, custom development, or another option. Which social media can be fully automated, and where might there be limitations. Estimated implementation timelines. Estimated cost. Examples of similar automations, if any. Additional Information: The Panatta brand represents premium Italian sports equipment for fitness clubs. Therefore, the texts must not only be translated but also adapted to the Ukrainian market, with the correct positioning: premium quality, Made in Italy, biomechanics, design, professional level of equipment. Links to our social media will be provided to the selected contractor.
Automation is needed for sending messages with a link to the KP on LinkedIn, WhatsApp, Reddit. Please describe how this will be implemented, the timeline, and the cost.
Set up ManyChat Pro + OpenAI API (model gpt-4o-mini, but you can suggest something niche). Without Make/Zapier, if it can be implemented with ManyChat's internal tools, or with them if you justify the need.Bot operation logic:Triggered by any incoming message from a new client (including transitions from ads).The bot should conduct a dialogue based on the provided prompt (information about the company/prices/FAQ will be provided). The goal of the bot is to consult and collect contact details (phone) or lead to a specific purchase request.Conditions for transferring to a manager:The client directly requests a person/manager.The client has left contact details.The bot does not know the answer to the question.Action upon transfer: the bot must inform the client that it is connecting a person, send a notification, and definitely stop automation so that AI does not respond while the manager is working.Requirements for the executor:Show 1-2 similar casesSet up protection against hallucinations (so that the bot does not invent prices and does not promise what is not available).Deliver the finished bot, explain where to edit the prompt (instructions for AI) and where managers can view chats.
AI Model
A person is needed who understands the creation of UGC creatives using AI. Videos are needed with the SAME person, about 200-300. Price is negotiable.
Create a Chrome plugin for connecting to a proxy I am looking for a developer, possibly with AI who has successfully published similar plugins in the store just AI writing without development experience is not needed please send proposals regarding price and deadlines
Need to transfer the site from Figma + Webflow to code, possibly with AI. If it's possible to do it with AI, with 100% accuracy and without bugs, it's better to do it that way. Please write your price and what experience you have specifically with this task.
: We are looking for a 3D artist / AI video maker for an innovative AI-EdTech project (Radaastreya)Description: We are creating a large-scale media franchise and concept of an empathetic next-generation AI robot for teenagers — RADAASTREYA. The image is of a wise and bright 7-year-old girl, embodying divine wisdom and the "golden principle of AI" ("Every gentle touch is a new neural connection").Tasks (can be tailored to a specific specialist): Create/refine a high-quality 3D model of the girl-robot based on existing concept art. Generate short (10 seconds) photorealistic AI videos based on ready prompts, showing the interaction between the robot and teenagers.Who we are looking for: A creator with a strong portfolio in the fields of cyberpunk, futurism, or light fantasy. We value depth, the play of light, and emotions. We look forward to your portfolios and price range for short videos/models!
Language Our tech team speaks English, Russian and German. You can choose any of these languages for your text deliverable and the review call. ObjectiveWe operate production-ready AI and document workflows on n8n Cloud that integrate Salesforce with LLMs and document services. While the workflow logic itself is functional, our deployment pipeline is broken. Moving a workflow from DEV to TEST to PROD—and connecting n8n to our changing Salesforce sandboxes—currently requires manual intervention and has been completely blocked for several weeks.We are engaging an experienced integration architect to review our current setup. The primary focus is to establish a reliable, automated deployment process for our existing features. Simultaneously, because we have experienced significant downsides with n8n, this review must deliver a clear strengths vs. weaknesses analysis of n8n to help us decide whether to commit to it or adopt a new tech stack for future features.This is a scoped evaluation engagement.No implementation work is included.Scope of DeliverablesA written assessment of two to four pages, followed by one review call. The assessment must address three core questions: Retain vs. Replace (with Strengths & Weaknesses Analysis):Provide a clear-eyed evaluation of n8n’s capabilities and limitations regarding our environment topology. We need to decide if we stick with n8n or transition future features to an alternative stack (e.g., self-hosted n8n, a higher n8n tier, or a different orchestration/code-first platform). Include a definitive strategic recommendation and its technical rationale. Licensing & Platform Constraints:Investigate and map the precise platform limitations and licensing boundaries of our current tier versus alternative tiers or self-hosted variations. The review must address: Feature & License Alignment:Analyze whether our current deployment blockages are caused by absolute platform restrictions, process-based misconfigurations, or native tooling limitations. Do not assume a higher license tier is the default or necessary solution. Native Environments & Git Functionality:Provide an objective assessment of n8n's native source control and environment promotion features (including explicit availability, gating criteria, and limitations when coordinating multiple projects/workspaces). Cost-Benefit and Feature Mapping:If specific deployment or environment automation features require an upgrade, identify the precise feature names, technical constraints, and current pricing structure. Weigh this clearly against the operational cost and overhead of moving to a self-hosted architecture. The Critical Path: For existing features:Define the immediate, actionable steps required to make our current deployment process automated and reliable. For future features:If a replacement stack is recommended, define the high-level target architecture.Out of Scope:Detailed migration project plans, granular effort estimation, and hands-on code changes. We require expert technical judgment, not a project management plan.Company and System ContextOur core system is Salesforce, customized with a managed recruitment package. n8n acts as our middleware, handling the heavy lifting for AI processing and integration logic triggered by Salesforce.Currently, we run three environments within a single n8n Cloud workspace split into three projects: Environment n8n Project Salesforce Target DEV DEV Developer Sandbox TEST TEST UAT Sandbox PROD PROD Production Workflow ArchitectureOur pipeline includes a three main LLM supported tools, and shared sub-workflows handling Salesforce authentication and HMAC verification.Workflows share identical names across all three projects. Environment-specific values (Client IDs, secrets) are isolated as project variables so that the core workflow logic remains uniform. Salesforce requests to n8n are secured via HMAC headers, and n8n authenticates back to Salesforce via OAuth. The system is designed to be dynamic: Salesforce passes its own instance URL in the webhook payload, meaning n8n should not need hardcoded sandbox URLs.Current Deployment Process & Known IssuesPromotion between environments is handled project-to-project via an in-house Bash script that transfers the workflow JSON and attempts to remap credential IDs to the target project. A Git repository is used for version control and code review, but it is entirely decoupled from the deployment pipeline; synchronization between Git and n8n is entirely manual.This custom scripting was built because native environment promotion features appear locked behind higher enterprise license tiers.Critical Bottlenecks: Deployment Blockage:End-to-end promotion from DEV to PROD is completely stalled and has been for several weeks. Opaque Root Cause:It is currently ambiguous whether our blockers stem from platform license limits, tooling deficiencies, or internal process gaps. Environment Serialization:Connecting n8n to a new Salesforce sandbox demands manual reconfiguration, creating a severe bottleneck that serializes developer workflows. Manual Friction:Multiple post-promotion steps still require manual intervention, and we lack clarity on which steps are hard platform constraints versus addressable automation gaps.Required Expertise Deep production experience with n8n in both Cloud and self-hosted environments, including precise knowledge of license tier gating. Extensive experience integrating n8n with Salesforce via OAuth, specifically managing sandbox-to-production lifecycles and webhook security (HMAC). A pragmatic approach to architecture, with a willingness to recommend deprecating custom-built in-house scripting in favor of robust automation. Exceptional technical communication skills, with the ability to translate complex architectural trade-offs into clear English for a non-engineering Product Owner.Provided Upon EngagementRead access to the n8n workspace, a Salesforce testing sandbox, our internal process documentation, and the Git repository. A dedicated technical contact for both Salesforce and n8n sides will be available. Note: Credentials and production secrets are strictly excluded from this public posting.Proposal RequirementsTo be considered, your proposal must explicitly address the following three points:
We need a specialist who has experience in creating automated monitoring systems for websites, news, competitor pages, and industry sources. A simple MVP scenario needs to be developed that will: regularly check a specified list of websites; find new publications, changes on pages, new documents, or updates; briefly analyze the content using AI; classify findings by types: news, product, partnership, vacancy, tender, report, important signal; record the results in Google Sheets / Airtable / Notion; send a brief digest via Telegram or email. Possible tools: n8n, Make, Zapier, Browse.ai, Apify, Perplexity API, OpenAI API, Google Sheets, Airtable, Telegram Bot. Important: Not just integration "parsed → recorded" is needed, but logic for selecting useful signals: what is important, what is noise, what requires action. Expected results: A working MVP on 10+ sources. A table with monitoring results. AI-generated brief summary for each found signal. Signal categorization. Automatic notification in Telegram/email. Instructions on how to add new websites. In your response, please include: whether you have created similar systems before; an example of a similar case without NDA details; how you would propose to implement the MVP; an estimated budget and timeline. We need to start with a simple MVP. If the result is of high quality, long-term refinement may be possible.
Generate a video clip from the rendering of a building using the object photo according to the reference and with a stunning scenario. There is a developed test prompt that needs to be refined. Possible neural networks for generation: King AI, Runway, Luma, Google AI Pro, Google AI Ultra. But this is not certain, you can suggest your own)
Need an AI Automation Engineer, a specialist for creating a system for active client search and smart outreach (not a regular chatbot-autoresponder) for a B2B project Data collection: automatic parsing of contacts from "blind" databases by name. Smart mailing: integration of Claude/OpenAI for analyzing client websites and generating hyper-personalized emails. Touchpoint funnels: setting up secure multi-step sequences (Follow-up) in LinkedIn and Email with protection against bans - (???) CRM integration: dynamic transfer and tagging of leads in CRM. Work format: phased payment for each successfully implemented module.
Development of a high-load system with fine-tuning of LLM models for an online service of multimodal product search by photo and text query simultaneously integrated into messengers through a personal agent-assistant.