{"schemaVersion":"jobsearcher.job.v1","id":"9a5aa96322b3ebd53274c822","url":"https://jobsearcher.com/jobs/9a5aa96322b3ebd53274c822","canonicalUrl":"https://jobsearcher.com/jobs/9a5aa96322b3ebd53274c822","title":"Python Developer","description":"Python / Agentic AI DeveloperLocation: Concord, CAWork Arrangement: OnsiteInterview: 1-hour in-person interview, including coding assessmentJob SummaryThe ideal candidate is a hands-on Python engineer with strong Agentic AI expertise who can take an AI solution from architecture and development through production deployment. The candidate should have strong backend engineering fundamentals and practical experience building reliable LLM/RAG systems, intelligent agents, and scalable microservices.Note: The interview is an in-person 1-hour interview with a coding assessment, so candidates should be prepared to demonstrate strong Python and software engineering skills.This is an excellent opportunity for a hands-on engineer to work with emerging AI technologies while taking significant technical ownership of solutions moving toward production.Key ResponsibilitiesDesign, develop, and deploy production-ready Agentic AI applications using Python.Build intelligent agents and AI-driven workflows to automate marketing campaign activities.Develop scalable microservices and REST APIs using Python and FastAPI.Design and implement LLM-powered applications using RAG, embeddings, semantic search, and vector databases.Develop multi-agent architectures and AI orchestration workflows.Integrate LLMs with enterprise applications, APIs, databases, and external services.Implement AI guardrails for security, reliability, accuracy, responsible AI, and controlled agent behavior.Build robust data and retrieval pipelines supporting RAG applications.Design distributed services that can scale reliably in production environments.Work with MongoDB for application data, metadata, conversation history, and AI-related workloads.Develop automated tests and maintain high standards for code quality and reliability.Participate in code reviews and contribute to software engineering standards and best practices.Troubleshoot and optimize AI applications and backend services.Collaborate with product managers, engineers, data scientists, and business stakeholders.Support deployment, monitoring, debugging, and production operations of Agentic AI solutions.Evaluate emerging AI models, frameworks, tools, and development approaches.Required Qualifications5+ years of strong hands-on Python development experience.5+ years of microservices and API development experience.5+ years of MongoDB experience.Strong experience developing REST APIs using Python.Hands-on experience with FastAPI.Strong understanding of distributed systems and microservices architecture.Strong experience with LLMs and Retrieval-Augmented Generation (RAG).Hands-on experience with vector databases, embeddings, and semantic search.Experience implementing AI/Agent guardrails.Strong Software Engineering Fundamentals, IncludingObject-oriented programmingDesign patternsUnit testingError handlingLoggingCode reviewsCI/CDStrong understanding of production software development and deployment practices.Preferred Qualifications2+ years of hands-on Agentic AI experience.Experience building multi-agent systems.Experience with AI orchestration frameworks and agent workflows.Experience deploying Agentic AI solutions into production.Experience with AWS, Azure, or GCP.Experience with AI coding/development tools.Experience with prompt engineering and LLM evaluation.Experience with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar frameworks.Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, or pgvector.Experience with Docker, Kubernetes, and cloud-native deployments.Experience with observability and monitoring of AI/LLM applications.Experience implementing authentication, authorization, secrets management, and API security.Technical EnvironmentProgramming: PythonAPIs: REST, FastAPIArchitecture: Microservices, Distributed Systems, Multi-Agent ArchitectureAI: LLMs, Agentic AI, RAG, Prompt Engineering, AI GuardrailsSearch & Retrieval: Embeddings, Vector Databases, Semantic SearchDatabase: MongoDBCloud: AWS / Azure / GCPDevOps: Docker, Kubernetes, CI/CDAI Frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel or equivalentSkills: llm,microservices,rag,python,agentic ai,vector,databases","company":"Staffing Spot","rawCompany":"staffing spot","city":"Concord","state":"NC","isRemote":false,"isActive":false,"createdAt":"2026-08-22T10:37:08.587Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"},{"code":"15-1254.00","title":"Web Developers","slug":"web-developers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Python Developer","description":"Python / Agentic AI DeveloperLocation: Concord, CAWork Arrangement: OnsiteInterview: 1-hour in-person interview, including coding assessmentJob SummaryThe ideal candidate is a hands-on Python engineer with strong Agentic AI expertise who can take an AI solution from architecture and development through production deployment. The candidate should have strong backend engineering fundamentals and practical experience building reliable LLM/RAG systems, intelligent agents, and scalable microservices.Note: The interview is an in-person 1-hour interview with a coding assessment, so candidates should be prepared to demonstrate strong Python and software engineering skills.This is an excellent opportunity for a hands-on engineer to work with emerging AI technologies while taking significant technical ownership of solutions moving toward production.Key ResponsibilitiesDesign, develop, and deploy production-ready Agentic AI applications using Python.Build intelligent agents and AI-driven workflows to automate marketing campaign activities.Develop scalable microservices and REST APIs using Python and FastAPI.Design and implement LLM-powered applications using RAG, embeddings, semantic search, and vector databases.Develop multi-agent architectures and AI orchestration workflows.Integrate LLMs with enterprise applications, APIs, databases, and external services.Implement AI guardrails for security, reliability, accuracy, responsible AI, and controlled agent behavior.Build robust data and retrieval pipelines supporting RAG applications.Design distributed services that can scale reliably in production environments.Work with MongoDB for application data, metadata, conversation history, and AI-related workloads.Develop automated tests and maintain high standards for code quality and reliability.Participate in code reviews and contribute to software engineering standards and best practices.Troubleshoot and optimize AI applications and backend services.Collaborate with product managers, engineers, data scientists, and business stakeholders.Support deployment, monitoring, debugging, and production operations of Agentic AI solutions.Evaluate emerging AI models, frameworks, tools, and development approaches.Required Qualifications5+ years of strong hands-on Python development experience.5+ years of microservices and API development experience.5+ years of MongoDB experience.Strong experience developing REST APIs using Python.Hands-on experience with FastAPI.Strong understanding of distributed systems and microservices architecture.Strong experience with LLMs and Retrieval-Augmented Generation (RAG).Hands-on experience with vector databases, embeddings, and semantic search.Experience implementing AI/Agent guardrails.Strong Software Engineering Fundamentals, IncludingObject-oriented programmingDesign patternsUnit testingError handlingLoggingCode reviewsCI/CDStrong understanding of production software development and deployment practices.Preferred Qualifications2+ years of hands-on Agentic AI experience.Experience building multi-agent systems.Experience with AI orchestration frameworks and agent workflows.Experience deploying Agentic AI solutions into production.Experience with AWS, Azure, or GCP.Experience with AI coding/development tools.Experience with prompt engineering and LLM evaluation.Experience with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar frameworks.Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, or pgvector.Experience with Docker, Kubernetes, and cloud-native deployments.Experience with observability and monitoring of AI/LLM applications.Experience implementing authentication, authorization, secrets management, and API security.Technical EnvironmentProgramming: PythonAPIs: REST, FastAPIArchitecture: Microservices, Distributed Systems, Multi-Agent ArchitectureAI: LLMs, Agentic AI, RAG, Prompt Engineering, AI GuardrailsSearch & Retrieval: Embeddings, Vector Databases, Semantic SearchDatabase: MongoDBCloud: AWS / Azure / GCPDevOps: Docker, Kubernetes, CI/CDAI Frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel or equivalentSkills: llm,microservices,rag,python,agentic ai,vector,databases","datePosted":"2026-08-22T10:37:08.587Z","dateModified":"2026-08-22T10:37:08.587Z","hiringOrganization":{"@type":"Organization","name":"Staffing Spot","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Concord","addressRegion":"NC","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"9a5aa96322b3ebd53274c822"},"url":"https://jobsearcher.com/jobs/9a5aa96322b3ebd53274c822"}}