AI Automation Expert for Platform Integration

We are looking for an experienced AI Automation Expert / AI Engineer to help us integrate an AI-powered matching platform into our existing web application. The goal is to build a reliable AI matching system that can analyze user/business profiles, requirements, preferences, and other structured or unstructured data, then intelligently identify and rank the most relevant matches. Key Responsibilities ● Design and implement an AI-powered matching and recommendation engine ● Integrate LLMs and AI APIs such as OpenAI, Claude, or similar models ● Develop matching logic using a combination of AI/LLM reasoning, structured data, scoring, and business rules ● Build automated workflows for data processing, matching, notifications, and follow-ups ● Integrate AI services with our existing backend and APIs ● Work with structured databases and potentially unstructured documents/text ● Implement profile/data extraction and normalization where required ● Develop relevance scoring and ranking mechanisms ● Optimize AI prompts, workflows, and matching accuracy ● Implement API integrations and webhooks ● Add logging, monitoring, error handling, and fallback mechanisms ● Ensure the system is scalable, secure, and production-ready ● Collaborate with our existing development team to integrate the solution into the current platform Ideal Candidate We are looking for someone who has hands-on experience building AI automation and intelligent matching systems, not just basic chatbot integrations. You should have experience with: ● OpenAI API / GPT models ● Claude or other LLM APIs ● AI agents and automation workflows ● RAG and embeddings ● Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector ● Recommendation or matching engines ● API development and third-party integrations Python and/or Node.js/TypeScript ● PostgreSQL or similar relational databases ● Workflow automation tools such as n8n, Make, or Zapier ● Prompt engineering and structured LLM outputs ● AI evaluation and optimization What We Expect: The ideal developer should be able to understand our business requirements and translate them into a practical AI matching architecture rather than simply connecting an LLM API. You should be comfortable determining when to use: AI/LLM → Embeddings → Vector Search → Rule-Based Scoring → Ranking → Automated Actions and when a simpler deterministic approach is more appropriate. We are looking for someone who can take ownership of the AI integration from architecture through production deployment. Project Scope: The initial engagement will focus on: 1. Understanding the existing platform and matching requirements 2. Designing the AI matching architecture 3. Integrating the required AI/automation services 4. Building the matching and ranking engine 5. Connecting the engine with our existing backend 6. Testing and improving matching accuracy 7. Implementing automated workflows 8. Deploying and documenting the solution There is potential for long-term work if the initial implementation is successful. To Apply Please include: ● Examples of AI matching, recommendation, or automation systems you have built ● Your experience with OpenAI, Claude, embeddings, RAG, or vector databases ● Relevant GitHub, portfolio, or live project links ● Your preferred AI/automation stack ● Your hourly rate ● Your availability and expected weekly hours Please also briefly explain how you would approach building an AI-powered matching engine where accuracy and explainability are important. We are looking for a hands-on expert who can contribute technically and help us build a robust production-ready solution.