Agentic AI Engineer

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.