Senior AI Engineer - Agentic Systems

IBM

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Work type
On-site
Location
Chicago, IL
Posted
6 days ago

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Listed pay typically runs $1,000,000–$1,000,000 across 8 roles with salary data.

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TL;DR · Senior AI Engineer - Agentic Systems

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We are seeking a senior AI engineer to join our dynamic team in Chicago for an enterprise-level AI engagement. This role involves designing and deploying agentic AI workflows on Azure, focusing on prompt engineering, RAG pipelines, and production-grade model deployment. You will work independently with the client to gather requirements, demonstrate capabilities, and ensure seamless integration of AI solutions across various business units. Key responsibilities include developing robust orchestration patterns for agent systems, integrating AI capabilities into enterprise applications via APIs and microservices, and establishing monitoring frameworks to maintain performance in production. Ideal candidates have extensive experience with agentic frameworks like LangChain and LangGraph, vector databases such as Azure AI Search, and MLOps practices for scalable deployment. This role demands a high level of technical proficiency and the ability to operate effectively without constant oversight.

Skills

Azure Agentic AI Prompt Engineering RAG Pipelines LangChain LangGraph MLOps CI/CD Azure ML Azure AI Studio API Integration Event-Driven Pipelines Microservices Monitoring Evaluation Feedback Loops Security Scalability Compliance LLMs Vector Databases Enterprise APIs Workflow Platforms AI Governance Logging Guardrails Human-in-the-loop

What you'll do

  • Design, build, and deploy agentic AI workflows using Azure OpenAI.
  • Develop prompt engineering strategies to enhance model performance and reliability.
  • Build Retrieval-Augmented Generation (RAG) pipelines for context-aware responses.
  • Implement agent orchestration patterns including tool calling and memory management.
  • Deploy and manage AI models on Azure ML and AI Studio services.

What we're looking for

  • 7+ years of experience in designing, developing, and deploying scalable AI applications using LLMs.
  • Expertise in building and operationalizing agentic AI workflows with frameworks like LangChain and LangGraph.
  • Proficient in implementing prompt engineering strategies to optimize model performance and reliability.
  • Experience in establishing monitoring, evaluation, and feedback loops for production-grade AI models.
  • Strong skills in integrating AI capabilities into enterprise applications via APIs and microservices.
  • Knowledge of vector databases and RAG pipelines for context-aware AI responses.
  • Expertise in AI governance, security, logging, and human-in-the-loop controls.

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