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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Propertyvalue Quantum Technologies Llc - **Location:** Nevada, MO, United States - **Experience:** Expert - **Salary:** $145,600.0 - **Contract:** Temporary contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Databases, Continuous Integration, Data Governance, Graph Database, Information Retrieval, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Natural Language Processing, Neo4j, Open Source Technology, Performance Tuning, Redis, OpenAI, Tensorflow, Search Technologies, Software Deployment, Software Engineering, Unstructured Data, Enterprise Search, IBM Watson Health, Pinecone, Qdrant, Google Cloud, Enterprise Software Applications, Pytorch, LangChain, Fast Healthcare Interoperability Resources, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Hallucination Detection, Deep Learning, Model Validation, Llamaindex, Electronic Medical Records, Generative AI, Agentic-AI, Git, Pgvector, Kubernetes, Information Technology, HuggingFace, CrewAI, Health Level Seven International, Apache Kafka, Machine Learning Operations, FAISS, Evaluation of Large Language Models, Google Gemini, Graph RAG, Restful APIs, Terraform, Semantic Kernel, Software Version Control, Data Pipelines, Docker, Databricks, Microservices - **Published:** September 30, 2026 - **Apply:** https://www.dice.com/job-detail/a52f5e90-0c1b-4f7b-83bd-2419b3a705f1 ## About the Role * 6+ years of AI/ML or software engineering experience with production AI solutions. * Strong Python programming skills. * Strong knowledge of Machine Learning, Deep Learning, NLP, LLMs, and Generative AI. * Hands-on experience with RAG and/or Agentic AI. * Experience with vector databases such as Pinecone, FAISS, pgvector, Qdrant, or Azure AI Search. * Experience with LLM platforms/models such as OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source LLMs. * Experience with AWS, Azure, or Google Cloud Platform. * Strong understanding of REST APIs, microservices, Docker, Kubernetes, Git, and CI/CD. * Experience with model evaluation, monitoring, observability, and production troubleshooting. Healthcare Domain Experience : * Experience in Healthcare, Life Sciences, Medical Technology, Health Insurance, or Clinical Applications. * Knowledge of EHR/EMR, Claims, Clinical Notes, HL7/FHIR, ICD, CPT/HCPCS, and medical terminology. * Understanding of healthcare privacy, security, compliance, and data governance. * Knowledge of FHIR, SNOMED CT, LOINC, UMLS, or similar healthcare standards is preferred. * Experience with healthcare AI use cases such as clinical documentation, medical summarization, patient engagement, claims processing, care management, or clinical decision support is a plus. Preferred Technologies : * LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI * PyTorch, TensorFlow, Hugging Face * RAG, Graph RAG, Knowledge Graphs, Hybrid Search * Azure OpenAI, Azure AI Foundry, AWS Bedrock, SageMaker * Databricks, Spark, Snowflake * MLflow, PostgreSQL, MongoDB, Redis, Neo4j * Kafka, Terraform, Docker, Kubernetes * LLM evaluation/observability frameworks * Responsible AI, explainability, bias detection, and AI governance Education : Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. Key Skills : Python | Machine Learning | Deep Learning | Generative AI | LLM | RAG | Agentic AI | NLP | Healthcare AI | EHR/EMR | FHIR | HL7 | Claims | Prompt Engineering | Vector Databases | LangChain | LangGraph | Azure/AWS/Google Cloud Platform | MLOps | LLMOps | Docker | Kubernetes | CI/CD | Responsible AI | Healthcare Data Privacy ## Description We are seeking a Senior AI Engineer with strong hands-on experience in AI/ML, Generative AI, LLMs, RAG, Agentic AI, NLP, and healthcare solutions. The candidate will be responsible for designing, developing, deploying, and optimizing production-grade AI applications using healthcare data such as EHR/EMR, claims, clinical notes, and medical documents., * Design and develop scalable AI/ML and Generative AI solutions for healthcare use cases. * Build production-grade LLM applications, RAG pipelines, AI agents, and conversational AI systems. * Work with healthcare data including EHR/EMR, claims, clinical notes, medical documents, and structured/unstructured data. * Implement prompt engineering, embeddings, vector search, semantic search, reranking, and knowledge retrieval. * Develop agentic AI solutions using LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks. * Fine-tune/customize LLMs using LoRA, PEFT, or similar techniques. * Build data pipelines for AI/ML ingestion, preprocessing, enrichment, training, and evaluation. * Integrate AI applications with APIs, databases, enterprise applications, and healthcare platforms. * Implement LLM evaluation, observability, monitoring, guardrails, hallucination detection, and performance optimization. * Deploy solutions on AWS, Azure, or Google Cloud Platform using MLOps/LLMOps practices. * Implement CI/CD, model versioning, automated testing, monitoring, and production deployment. * Ensure compliance with HIPAA, healthcare security/privacy, data governance, and responsible AI requirements. * Provide technical leadership, architecture/design guidance, and mentor junior engineers. ## Related Videos - [Building Blocks of RAG: From Understanding to Implementation](https://www.wearedevelopers.com/videos/1249-building-blocks-of-rag-from-understanding-to-implementation) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) - [Unboxing the DeepFace](https://www.wearedevelopers.com/videos/335-unboxing-the-deepface) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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