Agentic AI Engineer

Agentic AI Engineer
Designs scalable, secure enterprise AI solutions with Azure AI Foundry, OpenAI, retrieval architectures, and agent frameworks.

Job #137168

Office Professionals

Glendale, Arizona

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Talascend is currently seeking a Agentic AI Engineer for a contract opportunity with our client in Glendale, Arizona .


Overview


The role involves designing, building, and operating production-grade Agentic AI, decision intelligence, and retrieval-augmented generation solutions on Microsoft Azure. The engineer will support diverse business requirements by developing 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 reasoning, planning, tool usage, and workflow execution.
• Architect scalable generative AI solutions utilizing Azure AI Foundry, Azure OpenAI, Azure AI Search, and other AI services.
• Build and govern retrieval-augmented generation architectures using structured and unstructured enterprise data, 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 (NL2SQL) capabilities, ensuring accuracy, explainability, and secure access to enterprise data.
• Define semantic-layer strategies to facilitate AI understanding of enterprise data models, business metrics, terminology, and relationships.
• Architect decision intelligence systems that combine enterprise data, analytics, and AI reasoning to support informed decision-making.
• Establish reusable frameworks for impact analysis, dependency identification, prioritization, and decision traceability.
• Ensure AI-generated recommendations are explainable, evidence-based, grounded in authoritative data, and aligned with business goals.
• Define and automate evaluation methodologies for response quality, reasoning, relevance, business usefulness, and user trust, including golden datasets, LLM-based evaluation, and regression tests in CI/CD.
• Implement guardrails and responsible AI controls such as safety, PII protection, grounding checks, authorization boundaries, and human-in-the-loop escalation paths.
• Establish governance, security, safety, transparency, and compliance standards for AI solutions.
• Implement observability and monitoring frameworks including quality, performance, reliability, traces, tool calls, latency, token usage, and cost metrics.
• Optimize solutions for cost and latency through model selection, prompt engineering, caching, and workload sizing.
• Drive architectural decisions for scalable cloud-native AI platforms utilizing modern software engineering, DevOps, and MLOps/LLMOps practices.
• Define technical standards, reference architecture, and reusable frameworks for AI agents and decision-support applications.
• Mentor engineers and provide technical guidance on agentic AI, decision intelligence, and enterprise AI best practices.
• Partner with stakeholders and data teams to develop AI-ready data products, semantic models, and enterprise knowledge layers.
• Stay current with emerging AI technologies, frameworks, and industry practices, evaluating their applicability within enterprise environments.

Qualifications

• Bachelor's degree in computer science or related field (Master's 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 experience with agentic AI.
• Proficiency in Python; working knowledge of TypeScript/Node.js or similar languages.
• Experience designing and deploying production cloud applications.
• Hands-on experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector/hybrid search, and citations.
• Experience with agent frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or comparable systems, including multi-agent systems, tool calls, MCP, and human-in-the-loop.
• Experience building natural language to SQL solutions, including schema/metadata modeling and query grounding.
• Knowledge of decision intelligence systems integrating AI, analytics, and business context.
• Ability to evaluate and improve agent quality via prompt engineering, safety checks, and feedback loops.
• Strong understanding of MLOps, CI/CD, Docker, Kubernetes, observability, and production AI operations.
• Familiarity with core Azure services: AKS, Azure Container Apps, Functions, API Management, Entra ID, Key Vault, Azure SQL, or Cosmos DB.
• Experience with RESTful APIs, secure integrations, relational and NoSQL databases, and data engineering pipelines.
• Knowledge of enterprise AI deployment security, privacy, and compliance standards.
• Strong analytical, problem-solving, collaboration, and communication skills.

Preferred / Nice-to-Have

• Experience with Microsoft Fabric, Azure Databricks, or Azure Data Factory for AI data pipelines.
• Exposure to Copilot Studio, Power Platform, or Teams-based agent experiences.
• Experience with model fine-tuning, prompt caching, and token/cost optimization at scale.
• Supply chain domain knowledge or familiarity with ERP data such as Oracle EBS or SAP.
• Front-end experience with React and TypeScript for building agent interfaces.
• Microsoft certifications such as Azure AI Engineer Associate or Azure Solutions Architect.

We thank all applicants for their interest. However, only those qualified individuals who closely meet the qualifications of the position will be contacted. The details of the position are only a summary, other duties may be assigned as necessary.


Background Check and Drug Screen may be required.