Senior Agentic AI Engineer

Cynet Systems•Glendale, AZ

18h

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 the Digital Supply Chain Systems organization. The role develops reusable, scalable, secure, and explainable enterprise AI capabilities to support diverse and evolving supply chain business requirements. The engineer will architect agentic and generative AI systems, enterprise data retrieval and semantic-layer capabilities, and decision-support frameworks. The position also establishes evaluation, governance, safety, security, observability, and operational practices for enterprise AI applications. It includes driving cloud-native architecture and engineering standards, mentoring engineers, and partnering with business stakeholders, domain experts, and data engineering teams to turn complex requirements into reusable AI capabilities.

Requirements

Bachelor's degree in Computer Science or a related field; a Master's degree is preferred.
8+ years of relevant software engineering experience.
3+ years of hands-on experience developing generative AI, machine learning, intelligent automation, or LLM-based applications, including substantial recent Agentic AI experience.
Strong programming proficiency in Python and working knowledge of TypeScript/Node.js or a comparable language for application services.
Strong software engineering background, including 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 such as 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.

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/cost optimization at scale.
Supply chain domain knowledge, including 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).

Responsibilities

Design and build production-grade single-agent and multi-agent systems in Azure AI Foundry for reasoning, planning, tool use, 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, reliable integrations between agents and enterprise systems using MCP, REST APIs, relational databases, and event-driven services.
Design accurate, explainable, and secure Natural Language to SQL capabilities.
Define semantic-layer strategies so AI agents can 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 decisions.
Establish reusable frameworks for impact analysis, dependency identification, prioritization, recommendations, 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 quality, reasoning quality, recommendation relevance, business usefulness, and user trust, including golden datasets, LLM-based evaluation, and CI/CD regression evaluations.
Implement Responsible AI guardrails and controls for content safety, PII protection, grounding, authorization boundaries, and human-in-the-loop escalation.
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 solutions for cost and latency through model selection, prompt and context engineering, caching, and workload right-sizing.
Drive architecture 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 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, and evaluate their applicability in supply chain and enterprise environments.