Senior Agentic AI Engineer

Pay Range: $51.40hr - $66.71hr Job Overview:
Our client is seeking a hands-on Senior Agentic AI Engineer to design, build, and operate production-grade Agentic AI, Decision Intelligence, and Retrieval-Augmented Generation solutions on Microsoft Azure for the Digital Supply Chain Systems organization. The role will support diverse and evolving supply chain business requirements by developing reusable, scalable, secure, and explainable enterprise AI capabilities. Responsibilities:
Design and build production-grade single-agent and multi-agent systems in Azure AI Foundry capable of orchestrating reasoning, planning, tool usage, and workflow execution. Architect scalable Generative AI solutions leveraging Azure AI Foundry, Azure OpenAI, Azure AI Search, and other enterprise AI services. Build and govern Retrieval-Augmented Generation (RAG) architectures using structured and unstructured enterprise data sources, including embeddings, vector/hybrid search, chunking, metadata filtering, reranking, grounding, and citations.

Develop secure, reliable integrations between agents and enterprise systems using Model Context Protocol (MCP), REST APIs, relational databases, and event-driven services. Design Natural Language to SQL (NL2

SQL) capabilities, ensuring accuracy, explainability, and secure access to enterprise data. Define semantic-layer strategies that enable AI agents to understand enterprise data models, business metrics, terminology, and relationships. Architect decision intelligence capabilities that combine enterprise data, business context, analytics, and AI reasoning to support informed 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 methodologies for response quality, reasoning quality, recommendation relevance, business usefulness, and user trust, including golden datasets, LLM-based evaluation, and regression evals wired into CI/CD. Implement guardrails and Responsible AI controls: input/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. Implementation of observability and monitoring frameworks for AI applications, including quality, performance, reliability, and adoption metrics, plus traces, tool calls, latency, token usage, and cost.

Optimize solutions for 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, Dev Ops, 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 framework on Agentic AI, decision intelligence, software architecture, and enterprise AI best practices.

Partner with business stakeholders and domain experts to transform complex business requirements into reusable AI capabilities. Collaborate with data engineering teams to develop AI-ready data products, semantic models, metadata frameworks, and enterprise knowledge layers. Stay current with emerging AI technologies, frameworks, and industry practices, evaluating their applicability within supply chain and enterprise environments.

Education and Experience Requirements:

Bachelor's degree in Computer Science or a related field…