Senior Agentic AI Engineer

the Client•Glendale, AZ

12h•Hybrid

About The Position

The Senior Agentic AI Engineer will design, build, and operate production-grade Agentic AI, Decision Intelligence, and Retrieval-Augmented Generation (RAG) solutions on Microsoft Azure for a major organization’s Digital Supply Chain Systems (DSCS) division. The role supports evolving business requirements by developing reusable, scalable, secure, and explainable enterprise AI capabilities. The position is based in Glendale, Arizona, with a hybrid schedule requiring three onsite days per week. It is a 12-month contract paying $51.40–$66.71 per hour. U.S. citizenship and authorization to work for any U.S. employer are required; visa sponsorship is unavailable. The engineer will lead technical architecture and implementation for agentic systems, enterprise data and knowledge integrations, decision-support capabilities, evaluation and governance practices, and cloud-native AI platforms. The role also involves setting technical standards, mentoring engineers, and collaborating with business stakeholders, domain experts, and data engineering teams to convert complex requirements into reusable AI solutions. The client is described as a privately held global engineering, procurement, and construction leader with more than 125 years of history and over 25,000 infrastructure projects across 160 countries. GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation.

Requirements

Strong programming proficiency in Python and working knowledge of TypeScript/Node.js or a comparable language for application services.
Strong software engineering background, including experience designing and deploying production-grade cloud applications.
Hands-on experience building generative AI and RAG applications with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector or hybrid search, knowledge retrieval, grounding, and citations.
Experience with agentic AI frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or comparable frameworks, including single- and multi-agent systems, tool calling, MCP-based integration, and human-in-the-loop controls.
Experience building natural language to SQL or conversational analytics solutions, including schema and metadata modeling, query generation and validation, and grounding answers in retrieved data.
Experience designing or contributing to decision-intelligence systems combining AI, analytics, business context, and operational workflows to improve decision quality and business outcomes.
Experience evaluating and improving agent quality through prompt engineering, test datasets, LLM-based evaluation, safety checks, reasoning-quality assessment, and production feedback loops.
Strong knowledge of LLMOps, CI/CD, Docker and Kubernetes, observability, and production operations for AI applications.
Working knowledge of Azure platform services including AKS or Azure Container Apps, Azure Functions, API Management, Entra ID, Key Vault, and Azure SQL or Cosmos DB.
Good understanding of RESTful APIs, asynchronous patterns, secure integrations, relational databases, SQL, SQL/NoSQL data stores, and data engineering or ETL pipelines.
Experience with enterprise-scale secure AI deployments, including identity, authorization, data privacy, compliance, and production monitoring.
Strong analytical, problem-solving, collaboration, and communication skills.
Bachelor’s degree in Computer Science or a related field; a master’s degree is preferred.
At least 8 years of relevant software engineering experience.
At least 3 years of hands-on experience developing generative AI, machine learning, intelligent automation, or LLM-based applications, including substantial recent Agentic AI experience.
U.S. citizenship and authorization to work for any U.S. employer; the employer is unable to sponsor or take over sponsorship of an employment visa.

Nice To Haves

Experience with Microsoft Fabric, Azure Databricks, or Azure Data Factory for AI-ready data pipelines.
Exposure to Copilot Studio, Power Platform, or Teams-based agent experiences.
Experience with model fine-tuning such as LoRA/QLoRA, prompt caching, and token or cost optimization at scale.
Supply chain domain knowledge in procurement, expediting, logistics, or materials management, or familiarity with ERP data such as Oracle EBS or SAP.
Front-end experience with React and TypeScript for agent-facing user interfaces.
Microsoft certifications such as Azure AI Engineer Associate (AI-102/AI-103) or Azure Solutions Architect (AZ-305).
Master’s degree in Computer Science or a related field.

Responsibilities

Design and build production-grade single-agent and multi-agent systems in Azure AI Foundry to orchestrate reasoning, planning, tool usage, and workflow execution.
Architect scalable generative AI solutions using Azure AI Foundry, Azure OpenAI, Azure AI Search, and other enterprise AI services.
Build and govern RAG architectures over structured and unstructured enterprise data, including embeddings, vector and hybrid search, chunking, metadata filtering, reranking, grounding, and citations.
Develop secure and reliable agent integrations with enterprise systems using MCP, REST APIs, relational databases, and event-driven services.
Design Natural Language to SQL capabilities that provide accurate, explainable, and secure access to enterprise data.
Define semantic-layer strategies that help agents understand enterprise data models, metrics, terminology, and relationships.
Architect decision-intelligence capabilities combining enterprise data, business context, analytics, and AI reasoning to support decision-making.
Establish reusable frameworks for impact analysis, dependency identification, prioritization, recommendation generation, and decision traceability.
Ensure AI-generated recommendations are explainable, evidence-based, grounded in authoritative enterprise data, and aligned with business objectives.
Define and automate evaluation methods for response and reasoning quality, recommendation relevance, business usefulness, and user trust; create golden datasets and LLM-based and regression evaluations integrated with CI/CD.
Implement Responsible AI guardrails and controls, including input and output content safety, PII protection, grounding checks, authorization boundaries, and human-in-the-loop escalation paths.
Establish governance, security, safety, transparency, and compliance standards for enterprise AI solutions.
Implement observability and monitoring for AI application quality, performance, reliability, adoption, traces, tool calls, latency, token usage, and cost.
Optimize cost and latency through model selection, prompt and context engineering, caching, and workload right-sizing.
Drive architectural decisions for scalable cloud-native AI platforms using modern software engineering, DevOps, and MLOps/LLMOps practices.
Define technical standards, reference architecture, and reusable frameworks for AI agents, reasoning systems, and decision-support applications.
Mentor engineers and provide technical guidance on Agentic AI, decision intelligence, software architecture, and enterprise AI best practices.
Partner with business stakeholders and domain experts to turn complex requirements into reusable AI capabilities.
Collaborate with data engineering teams to develop AI-ready data products, semantic models, metadata frameworks, and enterprise knowledge layers.
Track emerging AI technologies, frameworks, and industry practices, and evaluate their applicability to DSCS and enterprise environments.