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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI RAG Engineer - **Company:** MatchPoint Solutions - **Location:** Maryland Heights, MO, United States (Remote available) - **Experience:** Experienced - **Salary:** $176,800.0 - $187,200.0 - **Contract:** Temporary contract - **Skills:** LangGraph Framework, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Cloud Computing, Code Review, Databases, Continuous Integration, Information Leak Prevention, Data Systems, DevOps, Elasticsearch, Information Extraction, Information Retrieval, Python (Programming Language), Knowledge Management, NoSQL, Open Source Technology, Parsing, E2e Testing, OpenAI, Cloud Services, Search Technologies, Software Construction, Software Deployment, Software Engineering, SQL Databases, Systems Integration, TypeScript, Unstructured Data, Management of Software Versions, Enterprise Search, Enterprise Data Management, Software Organization, Data Logging, Pinecone, Data Processing, Google Cloud, Enterprise Software Applications, Data Ingestion, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Llamaindex, Generative AI, Indexer, Agentic-AI, Git, Fastapi, Containerization, AI Platforms, Kubernetes, Information Technology, Weaviate, Milvus, Machine Learning Operations, Evaluation of Large Language Models, Invoking Functions, Google Gemini, Restful APIs, Semantic Kernel, Software Version Control, Data Pipelines, Automation Anywhere, OpenSearch, Docker, Microservices - **Published:** October 10, 2026 - **Apply:** https://www.careerjet.com/jobad/us6015d5715c0bd8be4a4d3f32935231c0 ## About the Role Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline, or equivalent practical experience. 5+ years of software development or related engineering experience. 3+ years of hands-on experience developing AI/ML or Generative AI applications. Strong Python development experience. Hands-on experience building RAG applications using LLMs, embeddings, and vector databases. Experience with LLM APIs such as OpenAI, Azure OpenAI, Anthropic, Google Gemini, or equivalent platforms. Experience with vector databases and search technologies such as Pinecone, Azure AI Search, OpenSearch, Elasticsearch, Weaviate, Milvus, or similar. Strong understanding of embeddings, semantic search, hybrid search, chunking, retrieval, reranking, and prompt engineering. Experience developing REST APIs and integrating enterprise systems. Experience with Git, automated testing, and software development best practices. Preferred Qualifications 7+ years of software engineering or related experience. 3 5+ years of hands-on Generative AI and LLM application development. Experience designing and implementing enterprise-scale RAG platforms. Experience developing agentic AI and multi-agent systems. Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks. Experience with AWS, Azure, or Google Cloud AI services. Experience with Docker and Kubernetes. Experience with MLOps, LLMOps, or AI application deployment. Experience with LLM evaluation frameworks and observability platforms. Experience with Azure OpenAI and the Microsoft AI ecosystem. Experience with enterprise search, knowledge management, or document intelligence. Experience working in regulated or highly secure enterprise environments. Experience leading complex AI and technology initiatives. Strong communication, stakeholder management, and technical leadership skills. Tools and Technologies Programming Languages: Python, SQL, JavaScript/TypeScript LLMs: OpenAI, Azure OpenAI, Anthropic, Google Gemini, open-source LLMs RAG Frameworks: LangChain, LangGraph, LlamaIndex, custom RAG pipelines Vector Databases and Search: Pinecone, Azure AI Search, OpenSearch, Elasticsearch, Weaviate, Milvus AI/ML: Embeddings, transformers, prompt engineering, reranking, model evaluation Agentic AI: Tool/function calling, AI agents, agent workflows, multi-agent systems Cloud Platforms: AWS, Microsoft Azure, Google Cloud Application Development: REST APIs, FastAPI, Docker, Kubernetes, Git Data Technologies: SQL, NoSQL, data pipelines, structured and unstructured data DevOps and Observability: CI/CD, monitoring, logging, tracing, observability ## Description We are seeking two experienced AI RAG Engineers to design, develop, and deploy production-grade Generative AI solutions, Retrieval-Augmented Generation (RAG) systems, LLM-powered applications, AI agents, knowledge retrieval platforms, and intelligent automation solutions. The ideal candidates will have strong hands-on experience with Python, Large Language Models (LLMs), vector databases, embeddings, prompt engineering, RAG architectures, REST APIs, cloud platforms, and AI application development. Experience with enterprise data, security, observability, and production deployment is highly desirable., 1. AI Solution Development Design, develop, and deploy production-grade Generative AI and RAG applications. Build end-to-end AI solutions using LLMs, embeddings, vector databases, retrieval pipelines, and enterprise data sources. Develop intelligent applications that combine LLM reasoning with enterprise knowledge and business processes. Implement document ingestion, parsing, chunking, embedding generation, indexing, retrieval, reranking, and response generation pipelines. Develop AI agents and agentic workflows that interact with tools, APIs, databases, and enterprise applications. Integrate LLM solutions with REST APIs, databases, cloud services, and existing enterprise systems. Evaluate and select appropriate LLMs, embedding models, vector stores, and AI frameworks based on business and technical requirements. Develop reusable AI components, services, APIs, and frameworks. Apply prompt engineering and structured output techniques to improve accuracy and reliability. 2. RAG Engineering Design scalable Retrieval-Augmented Generation architectures for enterprise use cases. Develop ingestion pipelines for structured and unstructured data. Implement semantic and hybrid search using vector databases and search technologies. Optimize document chunking, metadata strategies, embeddings, retrieval, filtering, and reranking. Improve retrieval accuracy, relevance, grounding, and response quality. Develop strategies for managing large knowledge bases and continuously changing enterprise data. Implement citations, source attribution, and traceability for AI-generated responses. Analyze retrieval failures and hallucinations and implement corrective improvements. Develop evaluation frameworks to measure retrieval quality, answer relevance, factuality, and task completion. 3. AI and Agentic Application Development Build LLM-powered applications, intelligent assistants, and enterprise AI solutions. Design agentic workflows using LangGraph, LangChain, Semantic Kernel, or similar frameworks. Implement tool/function calling and integrations with enterprise systems. Design multi-step workflows involving planning, retrieval, reasoning, tool execution, validation, and response generation. Implement agent memory and state management where appropriate. Develop guardrails to control agent behavior, tool access, and generated responses. Design human-in-the-loop workflows for high-risk or business-critical processes. Optimize AI applications for accuracy, latency, scalability, and cost. 4. Software Engineering Develop clean, maintainable, and production-ready Python applications. Build RESTful APIs and microservices supporting AI applications. Apply software engineering best practices, including version control, testing, code reviews, CI/CD, and documentation. Work with relational and NoSQL databases and data-processing technologies. Develop automated unit, integration, and end-to-end tests for AI applications. Collaborate with cloud and DevOps teams to deploy AI solutions into production. Troubleshoot application, data, model, retrieval, and integration issues. 5. Cloud, MLOps, and Production Support Deploy AI and RAG applications using AWS, Microsoft Azure, or Google Cloud. Containerize AI applications using Docker and deploy them using Kubernetes or equivalent technologies. Implement CI/CD pipelines for AI applications and services. Monitor model performance, application health, latency, token usage, retrieval quality, and cost. Implement logging, tracing, observability, and alerting for production AI systems. Support model versioning, prompt versioning, evaluation, and deployment processes. Participate in production support, incident resolution, and continuous improvement activities. 6. AI Quality, Security, and Governance Develop evaluation strategies for LLM and RAG applications. Establish metrics for retrieval accuracy, response quality, hallucination rate, latency, and cost. Implement responsible AI practices and appropriate safety controls. Protect sensitive enterprise data throughout AI workflows. Implement authentication, authorization, access controls, and data filtering. Address risks such as prompt injection, data leakage, insecure tool usage, and unauthorized access. Ensure AI solutions comply with applicable enterprise security, privacy, regulatory, and governance requirements. 7. Project Leadership and Collaboration Lead and collaborate with technical teams to deliver complex AI solutions. Work with business stakeholders, product owners, architects, data engineers, developers, and technology vendors. Translate business requirements into technical designs and AI solution architectures. Communicate complex AI concepts to technical and non-technical audiences. Provide technical guidance and mentorship to junior and mid-level engineers. Participate in architecture reviews, design sessions, technical planning, and solution demonstrations. Serve as a technical subject matter expert for AI, LLM, RAG, and agentic AI initiatives. Contribute to technical documentation, solution designs, architecture diagrams, and implementation standards.