Artist Opportunity Matching AI Collaboration

@starttechno

This project is a strong fit for my AI/LLM and data-platform experience. I would turn your several thousand annotated artist-match examples into a measurable proof of concept that goes beyond keyword overlap and can be compared directly with your current rule-based baseline. I’ll first normalize artist profiles, historical labels, and opportunity postings, then create a reproducible train/validation/test split to prevent leakage. The ranking pipeline can use embeddings for candidate retrieval, followed by structured LLM reranking with Claude, Gemini, and Grok experiments. I’ll test prompt strategies, profile/opportunity summaries, scoring rubrics, and—where the data supports it—lightweight fine-tuning or preference-based improvements. New postings can be ingested through an API or scraping layer, stored in MySQL, and exposed through Python scripts, notebooks, or a FastAPI endpoint. The evaluation will include Precision@K, Recall@K, NDCG, agreement with your annotations, and clear error analysis showing where each model succeeds or fails. You’ll receive the research plan, working prototype, baseline comparison report, prompt/model recommendations, and practical production-hardening notes. I have 10+ years of development experience and 249+ completed projects, including LLM integration, automation, Python, PHP, and MySQL systems. Are the annotated examples already available in CSV or JSON format? Muhammad Saad

@ZohaibRoy

⭐⭐⭐⭐⭐ Create an AI System to Match Artists with Opportunities ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a research-minded AI team. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in AI and machine learning. I will design an efficient approach using your existing dataset, experiment with large language models, and build a proof-of-concept pipeline to enhance match quality. ➡️ Why Me? I can easily develop your AI system for matching artists with opportunities as I have 5 years of experience in AI research, natural language processing, and machine learning. My expertise includes data analysis, model evaluation, and prototype development. Additionally, I have a strong grip on various large language models and can effectively implement fine-tuning techniques. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing with you in chat. ➡️ Skills & Experience: ✅ AI Research ✅ Natural Language Processing ✅ Machine Learning ✅ Data Analysis ✅ Model Evaluation ✅ Prototype Development ✅ Text Analysis ✅ Prompt Engineering ✅ Data Preprocessing ✅ API Development ✅ Conversational Reasoning ✅ Iterative Testing Waiting for your response! Best Regards, Zohaib

@KomalShaikh9200

✨Hi, I will be your social media marketing manager ✨ Hey, I'm Komal! I'm certified in social media marketing and content design. I've been working in marketing and managing pages for over ten years as part of a diverse team. "Wow! That's my company!" That is what I want you to say. I use social media as an integral part of nearly every digital marketing campaign. I help businesses access the best of social media and use it as a marketing asset." I will do : -BIO Setup and Page Optimization -Content Creation & Graphic Design -Paid Stock Images and Visual Content for Posts -High-Quality Branded Posts with your Logo & Website Link -Hashtag Research & SEO Indexing -Story Highlights -Cover Photos and Banners -Scheduling/Posting -Ongoing Support -IG & FB Reels, Short Videos, Stories, Posts, Pins and Tweets PLATFORMS: Instagram ! Facebook ! Pinterest ! Twitter(X) ! LinkedIn ! Google My Business Threads ! TikTok and YouTube My role as a social media marketing manager and content creator is to ensure that every post not only captures your audience's attention but also effectively drives marketing promotion, establishing your brand as a leader in its industry. I am waiting for your response. Thank you Komal :)

@WebTechDesignBG

Hello, I’m excited about the opportunity to collaborate on your artist opportunity matching AI project. You’re looking to leverage your dataset of artist profiles and historical matches to create a system that goes beyond simple keyword matching. My approach would involve experimenting with the latest large language models like Claude, Gemini, and Grok, focusing on nuanced text analysis and conversational reasoning to improve match quality. With 7+ years of experience in machine learning and prompt engineering, I can help design a clear research plan, develop a working prototype, and evaluate its performance against the existing baseline. I appreciate the need for clear communication throughout this process, ensuring findings are explained in straightforward language. Could you share more about the types of opportunities you envision pairing with the artists? Best regards, Ivan Mandinski

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@krupnovdenis

Hello! We can provide an external team to build and test this matching system. 1. Are you open to working with an external contractor or team for this project? 2. Which part should we tackle first: the research plan, prototype, or evaluation? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@FizzaNadeemK

Hi, I’ve worked on similar research-driven matching problems where the challenge is moving beyond keyword similarity toward meaningful semantic fit. With several thousand annotated examples, I’d first establish a reliable baseline, then test Claude, Gemini, and Grok using structured prompting, embeddings, reranking, and—where justified—fine-tuning. I’d build an evaluation pipeline with consistent train/test splits and metrics such as precision@K and recall@K, then iterate against the rule-based baseline. The deliverables would include a documented research plan, working prototype/API, reproducible experiments, and a plain-English performance report with production recommendations. Best Regards, Fizza

@Enazmul

Hello, I have 4+ years of experience in AI, LLMs, prompt engineering & data analysis. **How I will contribute** ✅ LLM-based artist matching ✅ Claude/Gemini/Grok experimentation ✅ Data preprocessing & evaluation ✅ Prototype pipeline/API development ✅ Baseline performance comparison I can build and iterate a research-driven matching system with measurable improvements. https://www.freelancer.com/u/Enazmul I would love to ask a few questions and share some relevant samples from my portfolio. Are you available for a quick chat? Best Regards, Md Nazmul Hasan

@Muhammadzeesha59

Hello Sir/MAM I am a Skilled Full Stack Developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms, Linux , Windows , Cloud , Azure , Ubuntu , OpenAI , Desktop Applications. Web Development I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning “Computer Vision ” Object Detection”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@ashinnd84

Hi, I read your post for "Artist Opportunity Matching AI Collaboration" and it lines up closely with the AI / ML work I do day to day. How I would approach it: 1. Agree the success metric before any modelling starts -- accuracy, latency, or cost per call -- so "done" means the same thing to both of us. 2. Stand up a small end-to-end baseline first. You see real output on your data early rather than at the end. 3. Iterate on the baseline, and hand over evaluation scripts plus notes so the numbers are reproducible on your side, not just mine. Directly relevant to your listed skills: AI Model Development, AI Chatbot Development, Prompt Engineering, Large Language Models (LLMs), PHP, SEO, Internet Marketing, MySQL, Large Language Model, Machine Learning (ML). My bid is $213 against your $30-250 range, and I can start straight away. Let's connect to discuss this further -- happy to walk you through the approach and cover anything you want nailed down before you decide. Thanks for reading. Best regards, Ashish & Team

@naseeb1995

With a decade-long track record in autonomy and AI-driven systems engineering, I'm the dynamic match you're looking for to power-up your artist matchmaking project. Leveraging my deep insights into AI Chatbot and Model Development as well as Machine Learning, I’m confident I can push the limits of the large-language-model families you mentioned for even richer-text analysis and conversational reasoning. Identifying teaching at scale pattern state-of-the-art is my specialty. Throughout my career, I’ve designed and implemented intelligent systems "that attract, qualify, and convert leads automatically," offering the data and fine-tuning skills necessary to optimize text-to-text related projects like yours. Lastly, my commitment to workable results translates into iterative rapid-paced efforts geared towards surpassing baseline standards. Besides delivering a documented research plan showcasing model choice and data preprocessing strategies, you can count on me for a detailed quantitative report comparing performance against the current rule-based baseline—in addition to practical recommendations for production hardening. My service brochure doesn't just cover a list of tedious skills but evidences how they can creatively be used toward unconventional business advancement. Let's reshape this challenging task together!

@Olywebart

I will leverage your annotated dataset to experiment with LLM families like Claude and Gemini for nuanced artist-opportunity matching. The focus will be on building a pipeline with robust data preprocessing, prompt engineering, and fine-tuning techniques to exceed the current rule-based accuracy. A research plan will guide model selection and evaluation strategies. The prototype will be structured as reusable scripts or an API, enabling quick iterations and clear performance comparisons. What are the current performance metrics of your rule-based baseline for comparison?

@shayankhan333

Hi, I can build your artist-to-opportunity matching pipeline using LLMs and show measurable improvement over your current rule-based baseline. I'll test Claude, Gemini and Grok with different prompt strategies on your annotated examples, comparing semantic matching against your baseline metrics. You will receive: Documented research plan with evaluation criteria Working prototype that ranks best-fit artists Performance comparison report with production recommendations I understand rapid iteration matters here. I've built OpenAI integrations and can have a first proof-of-concept ready within the first week. 1) Are your annotated examples in a structured format like CSV, or unstructured text? Best regards, Shayan

@mayankinnovative

Hello I reviewed your Artist Opportunity Matching AI Collaboration project and it stood out because you already have several thousand annotated examples which gives a strong foundation for testing whether LLM based matching can genuinely outperform the current rule based approach Problem Keyword based matching often misses context such as artistic style experience opportunity requirements and overall fit which can lead to weak recommendations Solution I can build a research focused pipeline that preprocesses your existing dataset tests Claude Gemini and Grok with structured prompts and semantic matching and ranks the best artists for new opportunities I will establish a reproducible baseline and evaluate the new approach using ranking metrics such as precision at K recall and MRR before deciding whether fine tuning or another approach is worthwhile Result You will receive a working prototype clear research documentation and a quantitative comparison showing which approach actually improves matching quality along with practical recommendations for production A few questions How are the historical matches labeled What format is the existing dataset in Do you already have a rule based baseline and its current performance metrics Should new opportunity data ingestion also be included in the prototype I can start immediately and provide regular progress updates throughout the project Thanks

@dtk507

Hello. I'm Davide, I'm here to help you. I understand you need an LLM-based matching pipeline that improves artist-to-opportunity ranking beyond rules. I'll structure this as an evaluation-first system: start by normalizing your annotated dataset, define a clean train/validation split, then compare Claude, Gemini, and Grok with prompt engineering and prompt templating in a reproducible Python workflow. I’d build a small API or notebook-driven pipeline that scores each posting against artist profiles, stores outputs in MySQL if needed, and measures lift with precision@k, MRR, and human review samples. The baseline/ranker comparison will make it easy to see where prompt engineering is enough and where fine-tuning or a reranker is justified. What format are the artist profiles, opportunities, and historical matches currently stored in, and how consistent is the annotation schema? I can start immediately and you can get good results within 4 days. Looking forward to hearing from you. Best regards, Davide

@mayr81

Great fit for building an LLM-driven matching system: your annotated artist dataset + historical matches is exactly what we need to beat a rule-based baseline. Approach: - Research plan: choose LLM families (Claude/Gemini/Grok) and define preprocessing, labeling schema, and an evaluation protocol (rank metrics like NDCG/MRR, plus calibration). - Prototype pipeline: preprocess artist profiles and new opportunity postings, then generate candidate retrieval with embeddings plus LLM-based re-ranking for nuanced fit. - Prompting/fine-tuning: iterate with structured prompts, consistency checks, and optional lightweight fine-tuning if patterns remain stable across validation. - Iteration loop: error taxonomy (style fit, constraints, fit-for-role language) to guide prompt/pipeline refinements. Deliverables align with your wishlist: documented plan, working ingest+rank prototype, and a quantitative report with production-hardening recommendations (latency, robustness to noisy postings, monitoring, and regression tests).

@rosscarter1011

Hello, If the LLM cannot beat your rule based matcher on several thousand real historical matches, then a smarter sounding model is not actually a better matching system. That is the part I would focus on first. I would use your annotated matches to build a proper evaluation set, establish exactly where the current baseline fails, then test profile and opportunity representations, ranking prompts, and different LLM families against the same measurements. This gives us a clear answer about whether Claude, Gemini, Grok, or another approach is genuinely improving match quality rather than just producing better explanations. Could we use your existing matches as the benchmark for the first prototype? How are the successful and unsuccessful matches currently labeled? have a nice day.

@engruhulajom

Hello There! I’m Md Ruhul Ajom, an experienced Full-Stack & AI Developer with 10+ years of experience building data-driven applications, AI workflows, APIs, and scalable backend systems. I understand you need an experimental LLM-based matching pipeline that learns from several thousand annotated artist/opportunity examples, ranks new opportunities against artist profiles, and demonstrably improves on your existing rule-based baseline. I am skilled in Python, OpenAI, Claude, Gemini, LangChain, prompt engineering, embeddings, semantic search, RAG, data preprocessing, evaluation pipelines, APIs, and automated testing. I’m comfortable with the $50/week arrangement and ready to start immediately. Looking forward to hearing from you. Best regards, Md Ruhul Ajom

@arturg6

Hello, "LLM‑Driven Artist‑Opportunity Matcher" - pairing creatives with fresh opportunities I will start with a clean data pipeline that tokenises each artist profile and opportunity, then uses a few-shot prompt with Claude or Gemini to rank matches; this approach balances speed and depth without heavy fine‑tuning. I delivered a similar LLM‑based lead‑ranking system for Australian PR contacts, see the results here: https://www.freelancer.com/projects/research/Australian-Sales-Leads/reviews To avoid over‑fitting, I will keep a hold‑out set and evaluate precision@k, adjusting prompts iteratively until the model beats your rule‑based baseline. Do you have a preferred evaluation metric (e.g., precision@k) and can you share the format of the annotated examples? Looking forward to working with you. Artur Giżycki