Artificial Intelligence Jobs in Irving, TX

Manage enterprise AI platform licenses, user access, token usage, budgets, vendor relationships, and adoption across tools including Claude, Copilot, ChatGPT Enterprise, and Gemini. Develop utilization, productivity, ROI, and cost-optimization reporting; coordinate onboarding and training; evaluate new AI capabilities; and partner with Finance, HR, IT, Security, and business leaders on governance, compliance, and responsible AI use.

Owns Five9’s back-office AI transformation agenda through opportunity diagnostics, hands-on automation pilots, value measurement, and scaling roadmaps. The role partners with Finance, HR, Legal, IT, Business Technology, and executives to identify high-value workflows, build or deploy AI and automation solutions, validate measurable outcomes, and establish future-state operating models. It requires direct experience shipping back-office automation, quantitative business-case development, cross-functional change leadership, and the ability to build internal capability rather than rely on outside providers.

Leads the architecture, development, and deployment of production-grade agentic AI platforms. Builds multi-agent systems, RAG pipelines, fine-tuning applications, MCP servers, and full-stack applications using Python, Java, React, and microservices. Integrates vector and graph databases, develops prompt and intent logic, and collaborates with business subject matter experts to automate enterprise workflows. Provides technical leadership, mentors engineers, and ensures scalable, secure, production-quality AI solutions.

Lead the architecture of Verizon’s enterprise AI platform across five layers, defining interfaces, standards, security, governance, observability, and scalability patterns. Conduct build-versus-buy evaluations and three-year TCO modeling, develop multi-cloud strategies, and architect LLM, agentic AI, and responsible AI capabilities. Create ADRs and reference architectures, guide reviews, and influence executive and engineering stakeholders. The role requires expertise in distributed systems, Kubernetes, LLM architecture, cloud platforms, API design, enterprise security, and agent orchestration.

Architect and build Verizon’s enterprise AI control plane, integrating AI developer platforms with governance hubs. Develop APIs, CI/CD governance pipelines, security and responsible AI guardrails, agent simulation, observability telemetry, IAM, and secure routing for autonomous workflows. Collaborate with platform engineering, DevOps, DataOps, data science, and product teams to operationalize models and agents. The role requires LLM operationalization, cloud architecture, platform engineering, applied ML, Python, Kubernetes, and automated safety testing expertise.

Leads engineering delivery for Verizon’s enterprise AI platform, including an AI Gateway and Agent Development Lifecycle. Manages 3–5 engineers, owns the roadmap, operates Kubernetes across GCP and AWS, delivers model routing and LLMOps capabilities, establishes developer SDKs and self-service onboarding, manages FinOps and platform SLAs, and drives secure, scalable agent deployment and enterprise adoption.

Leads enterprise AI/ML engineering strategy and governance, partnering with executive stakeholders, AI platform leaders, product owners, Legal, InfoSec, and Responsible AI teams. Directs Principal AI Engineers and Scientists, oversees production ML reliability and scalable data science code, and makes deployment decisions using governance frameworks, evaluation architectures, dashboards, and AI registration systems. Manages team planning and ensures compliance with corporate policies, AI safety standards, and risk frameworks.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.

Lead cloud and data platform engineering for analytics and AI: implement DevSecOps CI/CD pipelines, automate infrastructure with IaC, manage Snowflake data warehouse and ingestion, oversee cloud cost reporting and FinOps, and evaluate/operate emerging AI/LLM capabilities and governance. Act as senior individual contributor collaborating with security, finance, and engineering teams to optimize performance, security, and cost.

Owns TrendAI’s AI-generated answer visibility measurement program, including baselines, prompt methodology, recurring reporting, citation and competitive analysis, and entity representation. Translates findings into actionable content, PR, social, and messaging recommendations, partners with cross-functional teams to implement them, and evaluates outcomes. Requires enterprise B2B technology experience, AI visibility expertise, marketing analytics, knowledge-graph familiarity, technical fluency, and people-leadership readiness.

Leads Vizient’s AI Intelligent Automation organization by managing enterprise automation intake, prioritization, portfolio governance, roadmaps, workforce capacity, budgets, vendors, KPIs, and business value realization. Partners with executives, business stakeholders, and engineering leaders to align investments with enterprise objectives, manage delivery risks, ensure AI Development Lifecycle compliance, and improve organizational maturity and operational performance.

Designs and delivers enterprise generative and agentic AI applications using RAG, Graph RAG, knowledge graphs, context engineering, and multi-agent orchestration. The role integrates foundation-model APIs, tools, and data through MCP and A2A, while ensuring production scalability, observability, governance, privacy, guardrails, and reliability. Responsibilities also include real-time AI development, responsible AI compliance, mentoring junior developers, code reviews, and cross-functional collaboration.

Develop and maintain scalable cloud-native web, mobile, API, and data products; migrate services to public cloud; implement CI/CD and test automation; contribute to Agile teams, troubleshoot production issues, and promote secure, high-quality software practices.

Design and build scalable backend systems and cloud infrastructure for AI/ML applications, while implementing engineering standards and mentoring engineers.

Lead global marketing for Caris AI Insights, defining positioning, messaging, GTM, and commercialization. Drive product launches, sales enablement, thought leadership, customer evidence, competitive analysis, and cross-functional alignment to accelerate adoption of AI-driven oncology intelligence across healthcare, pharma, and research customers.

Lead strategy, design, and delivery of enterprise analytics and AI capabilities. Manage and grow data teams, build scalable analytics platforms, ensure data quality and governance, partner with business leaders to drive ROI, operationalize model lifecycle and responsible-AI practices, and oversee project delivery, performance monitoring, and continuous improvement.

Lead strategy, design, and delivery of enterprise AI platforms across customer, store, enterprise, and SDLC domains. Build production-grade GenAI/agentic systems, enable edge and real-time inference, establish MLOps/LLMOps and governance, and scale AI across thousands of stores to drive measurable business outcomes.

Lead research, prototyping, and delivery of AI/ML solutions for retail challenges. Manage ML project lifecycles, establish model development and MLOps best practices, mentor engineers, align initiatives with business stakeholders, oversee ML pipelines and infrastructure, ensure data quality and ethical AI, and communicate technical concepts to nontechnical audiences.

Designs and executes AI Quality Engineering strategies for enterprise AI applications, LLM solutions, intelligent automation, and agentic systems. Builds validation processes, evaluation frameworks, automated testing, runtime quality controls, observability, and reliability practices. Partners with engineering, clinical, security, governance, data, and product teams to validate healthcare workflows and responsible AI deployment. Coordinates Agile testing and release activities, supports production reliability, researches emerging technologies, and mentors Quality Engineers.

Lead development of ServiceNow solutions enhanced with Generative AI, AI agents, Graph RAG, and Agentic RAG. Responsibilities include building ServiceNow applications, workflows, integrations, RAG pipelines, knowledge graphs, and intelligent agents using LangGraph, LangChain, and Neo4j. The role requires Python and JavaScript development, enterprise data integration, security governance, testing, production troubleshooting, and collaboration with technical and business stakeholders.

Lead enterprise adoption of Databricks analytics and AI capabilities, partnering across Finance, Commercial, Operations, Digital, and Data Engineering. Own the strategy, roadmap, lifecycle, deployment, adoption, and value realization of business-facing data and AI products. Govern KPIs, semantic models, ontologies, metrics, data quality, and reporting standards. Enable AI-powered decision-making, self-service analytics, training, and change management while bridging business stakeholders and technical teams.

Lead end-to-end product management for enterprise security operations platforms, translating SOC/operator needs into roadmaps, user stories, and validated AI use cases. Manage vendors, enforce SLA and model validation, coordinate Agile delivery, UAT, adoption, and report platform and AI performance to senior leadership.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.