We are producing a documentary series and are seeking an experienced AI Visual Designer to help establish the visual identity of the project. This is not a traditional graphic design role. We need someone who specializes in AI-assisted visual development, cinematic concept design, documentary graphics, motion design, and visual storytelling. The goal is to create visual examples that allow our production team to determine: The overall visual style of the documentary AI-generated graphics approach Motion graphic direction On-screen information design Historical reenactment style Interview camera look and framing Color palette and cinematic treatment Deliverables Phase 1: Visual Style Exploration Create 3-5 distinct visual directions for the documentary including: Color palettes Typography recommendations Motion graphics style Documentary graphic package concepts Lower thirds concepts Episode title treatments Phase 2: AI Concept Frames Create sample visual frames demonstrating: Historical sequences Documentary storytelling graphics Animated map concepts Archival photo treatments Timeline graphics Social media graphics Phase 3: Camera & Production Look Development Provide visual examples showing: Interview lighting setups Camera framing options Color grading approaches Multi-camera interview looks Documentary cinematography references Sample scene mockups Phase 4: Final Look Book Deliver a visual presentation containing: Selected visual direction Style guide Graphic references Camera references Mood boards Production recommendations Required Skills: Midjourney Runway Flux ChatGPT Adobe Photoshop Adobe After Effects Documentary Design Motion Graphics Storyboarding AI Image Generation Preferred Experience Please provide examples of: Documentary graphics Broadcast graphics Streaming platform projects AI-generated visual development Mood boards Visual look books Cinematic concept art Budget We are seeking a highly skilled freelancer rather than a large agency. Please include: Portfolio link Examples of AI-generated visual development work Examples of documentary or cinematic projects Your favorite AI design tools Estimated timeline for completing all deliverables

Summary We are deploying a 100% offline, private AI stack using LibreChat, vLLM, and RAG. The project requires setting up a local infrastructure for AI model deployment, ensuring security and privacy. The ideal candidate will have experience in MLOps and DevOps, with a focus on local AI deployments. Responsibilities include managing the deployment process, optimizing performance, and ensuring the system's reliability and security. Title: MLOps/DevOps Engineer Needed to Deploy Private, Local AI Stack (LibreChat + vLLM + RAG) Project Description: We are deploying a 100% offline, privacy-first AI portal on local hardware (Threadripper server with Dual RTX 6000 Blackwell 96GB GPUs). Must be able to work at least in a hybrid environment, preferably in-person at our NYC office. We need an expert to containerize and automate this infrastructure. The stack consists of: LibreChat frontend, MongoDB, MeiliSearch, local HuggingFace RAG, and local SLMs (Llama 3/Mistral) served via vLLM. Key Responsibilities: Configure Ubuntu server host, including NVIDIA drivers and CUDA environment. Optimize multi-GPU serving utilizing vLLM for high-throughput local inference. Build out a localized, containerized RAG pipeline with HuggingFace. Write Docker Compose and Ansible configuration scripts for automated deployment.

We are an outsourced accounting and tax firm serving small businesses. We handle bookkeeping, accounts payable, expense management, financial reporting, and tax preparation. We're at a point where we want to seriously rethink how we work — not just add AI tools on top of old processes, but redesign our workflows from the ground up to take advantage of what AI can actually do. We're looking for a contractor who has done this before — someone who understands accounting and tax workflows deeply and also has hands-on experience implementing AI tools in professional services or finance environments. What you'll do: • Map our current accounting and tax workflows end-to-end • Identify where AI can replace, assist, or accelerate steps • Research and evaluate tools that fit our stack (we use QBO, and are open to others) • Work alongside our team to build and document the new workflows — not just hand us a deck • Help us implement and iterate until the workflows are actually running Who we're looking for: • Strong background in accounting, tax, or working closely with firms that do this work • Real experience deploying AI tools in a professional services or finance context — not just theoretical • Familiarity with tools like AI document processing, workflow automation, or AI-assisted review • Someone who can roll up their sleeves and build alongside us, not just consult from a distance This is a part-time engagement to start. If it goes well, there's room to expand the scope. We are serious about this — we want to move from buying tools to actually redesigning how we operate, and we need someone who has done it. In your application, please tell us: what accounting or tax workflows have you redesigned with AI, what tools were involved, and what the before/after looked like. Specifics matter more than credentials here.

Want someone to go over our Retell prompts / get best practices, must be a retell partnered agency. Please share previous projects and the name of your business on retells site.

We are a small, applied AI lab running a live, production-track AI product for an institutional financial services client. The work is technical, fast-moving, and high-stakes. We need a senior, collaborative engineer to own our data infrastructure layer. No corporate layers. Fast decisions. The Role & What You'll Own You will design, build, and maintain the hands-on engineering pipelines feeding a live AI scoring engine. In this agentic environment, data moves from 15+ heterogeneous external sources (APIs, PDFs, regulatory filings) through Bronze, Silver, and Gold layers.

I need someone to help me create an AI-generated launch video for a crypto product launching soon. This is meant to give people excitement about the opportunity. The ideal candidate should have experience in AI video generation and marketing to effectively convey the product's potential.

I am looking for an experienced ASR engineer to build a production-ready speech-to-text system for a low-resource language. I already have approximately 3,000 prepared audio segments, totaling about 10 hours of audio, with clean and consistent transcripts ready for immediate use. Data preparation and segmentation are already handled. The initial 10 hours of audio will serve as the first milestone. After that, the engineer will be expected to continue training and improving the model with additional data until the system reaches a target WER of 10% or below. Your responsibility will focus on: Fine-tuning a Whisper-based model for high transcription accuracy Optimizing word error rate (WER) over time Providing inline/embedded start timestamps per phrase Building an efficient inference pipeline for both real-time and batch transcription Structuring evaluation and improvement workflows Preparing the system for deployment and integration into a web platform Providing clear documentation and guidance so I can independently continue training and improving the model over time without ongoing engineer involvement The goal is to reach strong accuracy at launch, with a clear process for continued improvement as more data becomes available. Please describe your experience with Whisper fine-tuning or similar ASR model training in your proposal.

I need a web developer that can be trusted with my idea. I have built a website with Claude AI and need a developer to finish the following items and take it to the next level. The tasks involve completing the website, ensuring it is user-friendly, and optimizing for performance. The ideal candidate should have experience in web development and AI integration.

We are looking for a developer who speaks Spanish expert GoHighLevel (GHL) developer and automation specialist to build and maintain a premium "Dental Patient Rescue System" for high-ticket dental clinics in the US market. This is a long-term contract on a per-project basis. We close the clients; you deploy the technical infrastructure. Everything must be structured into a clean, clonable Snapshot for rapid deployment. The system consists of 3 core components that you must be able to build perfectly: 1. Missed Call Text Back with Conversational AI: Advanced GHL workflows with business vs. off-hours logic that triggers an AI SMS conversation to pre-qualify and book patients. 2. 24/7 Voice AI Receptionist: Integration of Vapi or Bland.ai with GHL to handle after-hours or busy-line emergency calls. The Voice AI must be capable of checking calendar availability in real-time and booking the appointment directly via GHL API. 3. Database Reactivation: Setting up drip/batch campaigns in GHL to reactivate old/cold leads safely without triggering carrier spam filters. Mandatory Requirements: .Proven experience with GHL sub-accounts, workflow logic, custom fields, and API integrations. .Deep understanding of US compliance: A2P 10DLC Campaign Registration, opt-in forms, and privacy policy setup to ensure high SMS deliverability. .Ability to deliver a 100% tested, working environment including phone number routing and A2P submission tracking. To Apply: Please reply with the word "SYSTEM" at the very beginning of your proposal so I know you read the whole post. Briefly describe your experience integrating Voice AI (Vapi/Bland.ai) with GoHighLevel and your success rate with A2P 10DLC approvals.

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.