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AI Engineer – Agentic AI

Job in Phoenix, Maricopa County, Arizona, 85003, USA
Listing for: CoreAi Consulting
Full Time position
Listed on 2026-08-22
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Backend Developer, Cloud Engineer - Software, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below

We are seeking an experienced AI Engineer with 5+ years of software engineering experience and 2+ years of hands-on expertise in building Agentic AI applications, LLM-powered solutions, and intelligent workflow automation. The ideal candidate will have strong Python development skills and experience designing enterprise AI solutions using modern LLM frameworks, retrieval-augmented generation (RAG), vector databases, and multi-agent architectures.

This role involves designing, developing, and deploying scalable AI applications that automate business processes, integrate with enterprise systems, and leverage autonomous AI agents to improve productivity and decision-making.

Key Responsibilities
  • Design and develop enterprise AI applications using Python and cloud-native architectures.
  • Build intelligent AI agents and multi-agent systems capable of reasoning, planning, tool usage, and autonomous task execution.
  • Develop GenAI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
  • Design and implement agent orchestration workflows using modern AI frameworks such as Lang Graph, Lang Chain or similar technologies.
  • Develop AI-powered assistants, copilots, and workflow automation solutions for enterprise business processes.
  • Build and maintain knowledge retrieval systems, embedding pipelines, semantic search, and contextual memory for AI applications.
  • Integrate AI agents with enterprise applications, REST APIs, databases, messaging systems, and third-party services.
  • Design secure and scalable APIs and microservices to support AI applications and agent workflows.
  • Optimize prompt engineering, context management, memory strategies, and tool selection to improve AI response quality.
  • Evaluate, benchmark, and integrate commercial and open-source LLMs based on business requirements.
  • Implement observability, monitoring, logging, evaluation, and guardrails for production AI systems.
  • Collaborate with product managers, architects, and engineering teams to translate business requirements into scalable AI solutions.
  • Deploy and manage AI applications on AWS, Azure, or GCP using modern Dev Ops and CI/CD practices.
Required Qualifications
  • 5+ years of software engineering experience with strong Python programming skills.
  • 2+ years of hands-on experience building AI applications using Large Language Models
  • Strong experience with Agentic AI concepts, autonomous agents, AI orchestration, and workflow automation.
  • Experience with AI frameworks such as Lang Graph, Lang Chain or similar.
  • Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
  • Experience with embeddings, semantic search, vector databases, and knowledge retrieval systems.
  • Experience working with vector databases such as Pinecone, Milvus, Weaviate, Chroma, Redis Vector, FAISS, Open Search, or similar.
  • Strong experience building REST APIs using FastAPI, Flask, or similar
  • Experience integrating AI applications with enterprise systems through REST APIs, GraphQL, messaging platforms, or event-driven architectures.
  • Experience with Docker, Kubernetes, Git, Git Hub Actions, and CI/CD pipelines.
  • Experience deploying cloud-native applications on AWS, Azure, or Google Cloud Platform.
  • Strong understanding of distributed systems, microservices, asynchronous programming, and event-driven architectures.
  • Experience implementing authentication, authorization, and secure AI application design.
  • Strong debugging, performance tuning, and production support experience for AI applications.
Preferred Qualifications
  • Experience with Model Context Protocol (MCP) servers and tool integration.
  • Experience with AI evaluation frameworks, observability platforms, and LLM monitoring tools.
  • Knowledge of prompt optimization, guardrails, hallucination mitigation, and AI safety best practices.
  • Exposure to AI-assisted software development tools such as Git Hub Copilot, Cursor, Claude Code, or similar.
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