Agentic AI Engineer

Integra • Dallas, TX

4d • Onsite

About The Position

The Agentic AI Engineer builds production-ready AI agents that address operational problems by evaluating information, recognizing conditions and exceptions, making decisions, and initiating actions across enterprise systems. The role identifies manual, inefficient, or difficult-to-scale processes across manufacturing, construction, finance, project controls, and operations, and turns them into AI-powered workflows. The engineer works with business teams and technology functions—including Data Engineering, Integration, DevSecOps, Cybersecurity, and Application Development—to automate work that traditional rules-based automation cannot readily handle. The position is 100% onsite in the North Dallas/Plano area.

The role designs, builds, deploys, and maintains single-agent and multi-agent solutions, copilots, conversational applications, and intelligent workflows using Microsoft Azure AI capabilities, Microsoft Copilot technologies, and code-first orchestration frameworks. It implements agent planning, tool use, function calling, memory, context management, routing, delegation, reflection, and human-in-the-loop controls, with bounded autonomy, approval checkpoints, escalation paths, timeouts, rollback, and compensation mechanisms.

The engineer develops retrieval-augmented generation solutions grounded in trusted enterprise information, integrating structured and unstructured data from Microsoft Fabric, data lakes, databases, SharePoint, enterprise repositories, APIs, and SaaS systems. The work includes indexing, chunking, metadata, embeddings, hybrid and semantic search, reranking, citations, and permission-aware retrieval. It also builds secure integrations and governed agent actions across enterprise applications and systems, while preserving system-of-record controls, approval processes, data entitlements, and integration standards.

The position partners with business stakeholders to discover and prioritize process automation opportunities, map processes and exceptions, define human-review requirements, validate pilots, measure benefits, and move successful solutions into governed production. It establishes evaluation, security, responsible AI, monitoring, and operational practices—including regression testing, red-team testing, telemetry, audit trails, release controls, incident procedures, and continuous improvement. The engineer also collaborates across technical, business, risk, legal, and compliance teams; communicates capabilities and limitations; creates documentation and training; and mentors engineers.

The role requires a bachelor's degree in a relevant discipline or equivalent professional experience, at least five years of experience in a relevant engineering or enterprise-development field, and at least two years of hands-on experience with generative AI, RAG, copilots, or agent-based solutions. Candidates should have experience taking AI prototypes into secure, monitored production solutions, strong Python or C# skills, and experience integrating AI with APIs, enterprise data, workflows, and business applications.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Information Systems, or a related discipline, or equivalent professional experience.
• At least 5 years of experience in software engineering, AI engineering, data engineering, automation, or enterprise application development.
• At least 2 years of hands-on experience building generative AI, retrieval-augmented generation, copilots, or agent-based solutions.
• Proven experience moving AI prototypes into secure, monitored, production-grade enterprise solutions.
• Strong Python or C# development skills and experience integrating AI solutions with APIs, enterprise data, workflows, and business applications.
• Strong analytical, process-discovery, facilitation, documentation, and stakeholder-management capabilities.
• Experience with Microsoft Foundry, Foundry Agent Service, Azure OpenAI, Microsoft Copilot Studio, Azure AI Search, and Azure AI Content Safety.
• Experience with OpenAI GPT and Anthropic Claude models and agent orchestration using Microsoft Agent Framework, Semantic Kernel, or comparable frameworks.
• Knowledge of prompt engineering, structured outputs, function calling, tool use, context management, memory, multi-agent patterns, evaluation, guardrails, safety testing, and production monitoring.
• Knowledge of RAG, embeddings, vector search, semantic and hybrid retrieval, reranking, document ingestion, indexing, permissions filtering, and citations.
• Knowledge of Python and/or C#/.NET, REST APIs, OpenAPI, JSON, asynchronous programming, SQL, NoSQL concepts, caching, Docker, serverless architectures, Git, CI/CD, and automated testing.
• Knowledge of Microsoft Entra ID, managed identities, Azure Key Vault, RBAC, Azure Monitor, Application Insights, Log Analytics, Microsoft Fabric, and Power BI.

Nice To Haves

• Experience integrating AI with ERP, project controls, construction management, manufacturing, CRM, HCM, procurement, content, and analytics platforms.
• Practical knowledge of manufacturing, supply chain, construction, project controls, finance, ERP, workflow analysis, and continuous improvement.
• Experience with Oracle Fusion ERP, Deltek Costpoint, SAP S/4HANA, Microsoft Dynamics 365, Primavera P6, Procore, Autodesk Construction Cloud, Salesforce, ServiceNow, SharePoint, Teams, Azure Databricks, or ADLS Gen2.
• Ability to distinguish work that should be assisted, automated, escalated, or retained under human control.

Responsibilities

• Design, build, deploy, and maintain enterprise AI agents, copilots, conversational applications, and intelligent workflow solutions.
• Create single-agent and multi-agent architectures using managed agent services, code-first frameworks, and Microsoft Copilot technologies.
• Implement agent planning, tool use, function calling, memory, context management, routing, delegation, reflection, and human-in-the-loop controls.
• Design bounded autonomy, approval checkpoints, escalation paths, timeout behavior, rollback, and compensation mechanisms.
• Create reusable agent components, prompt libraries, tools, connectors, policies, templates, and reference architectures.
• Build solutions using Microsoft Foundry, Foundry Agent Service, Azure OpenAI, Azure AI Search, Azure AI Content Safety, and related Azure AI capabilities.
• Develop enterprise agents using Microsoft Copilot Studio and integrate them with Microsoft 365, Teams, Power Platform, and approved enterprise channels.
• Use Microsoft Agent Framework, Semantic Kernel, or comparable orchestration frameworks for code-first agent development.
• Evaluate and route approved OpenAI GPT, Anthropic Claude, and Microsoft-hosted models based on quality, latency, cost, safety, reliability, data handling, and task fit.
• Implement model abstraction, fallback, retry, timeout, graceful degradation, structured output, function calling, and governed model-version management.
• Design RAG solutions and pipelines for indexing, chunking, metadata, embeddings, hybrid and semantic search, reranking, citations, and access filtering.
• Integrate structured and unstructured enterprise knowledge from Microsoft Fabric, data lakes, databases, SharePoint, repositories, APIs, and SaaS systems; ensure responses are grounded, traceable, current, permission-aware, and supported by source references.
• Collaborate with data owners and stewards to define authoritative sources, business terminology, metadata, and knowledge-quality standards.
• Partner with business leaders and subject-matter experts to identify repetitive work, decision bottlenecks, inefficiencies, control gaps, and automation opportunities.
• Map current-state and future-state processes, including decisions, exceptions, roles, data inputs, systems, business rules, and expected outcomes.
• Prioritize use cases based on business value, feasibility, risk, data readiness, adoption, operating cost, and measurable return.
• Define when human review is required before agents communicate externally, change systems of record, commit funds, or affect operations.
• Create pilots, validate outcomes, measure benefits, and transition successful solutions into governed production services.
• Build secure tools and connectors for enterprise applications, APIs, workflows, databases, message platforms, and approved automation services.
• Integrate agents with enterprise platforms such as Oracle Fusion ERP, Deltek Costpoint, Primavera P6, Procore, Salesforce, manufacturing systems, Microsoft Fabric, and Power BI, as applicable.
• Use Azure API Management, Azure Functions, Logic Apps, Service Bus, Event Grid, and containerized services to expose governed agent actions.
• Apply least privilege, user-delegated authorization, transaction boundaries, idempotency, validation, rate limits, audit logging, and business-rule enforcement.
• Prevent agents from bypassing system-of-record controls, approval processes, data entitlements, or enterprise integration standards.
• Define evaluation frameworks and representative datasets for groundedness, relevance, correctness, completeness, tool selection, task completion, consistency, safety, latency, and cost; create automated regression tests.
• Conduct red-team testing for prompt injection, data exfiltration, unauthorized actions, harmful outputs, hallucinations, and tool misuse.
• Apply responsible AI, privacy, security, compliance, transparency, accountability, and human-oversight principles throughout the solution lifecycle.
• Implement content filtering, access controls, data-loss prevention, audit trails, release gates, canary rollouts, and rollback capabilities.
• Implement telemetry for prompts, responses, retrieved context, tool calls, workflow steps, approvals, errors, token usage, latency, cost, and user feedback.
• Use Azure Monitor, Application Insights, Log Analytics, and AI-specific tracing and evaluation capabilities.
• Monitor quality drift, retrieval degradation, model changes, safety events, tool failures, adoption, and realized business value.
• Troubleshoot production AI solutions, conduct root-cause analysis, and implement corrective actions.
• Create dashboards, alerts, runbooks, support procedures, service-level expectations, incident processes, and recovery plans.
• Collaborate with business and technology teams to define AI use cases, acceptance criteria, delivery roadmaps, and measurable value.
• Partner with Data Engineering, Integration, DevSecOps, Cybersecurity, Legal, Compliance, Risk, Internal Audit, and data-governance stakeholders.
• Communicate AI capabilities, limitations, risks, and trade-offs to technical and nontechnical stakeholders.
• Create documentation, demonstrations, training materials, patterns, and standards to improve enterprise AI literacy.
• Mentor engineers and contribute to an enterprise community of practice for applied and agentic AI.

Benefits

• Competitive pay with incentive opportunities that recognize results and contributions.
• Medical, dental, and vision coverage.
• Life and disability insurance programs.
• Voluntary benefits options to personalize coverage.
• Generous paid time off.
• Education assistance and tuition support.
• Retirement and savings programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level