Artificial Intelligence Jobs in Irving, TX

Design, develop, test, operate, and maintain AI/GenAI-enabled full-stack applications. Build production-ready code, implement scalable architectures, integrate LLM platforms, ensure security/compliance, perform code reviews, improve reliability, and collaborate across product, architecture, and business teams.

Build scalable Generative AI applications and microservices in an agile environment. Translate business requirements into high-quality code, participate in testing, CI/CD and deployments, and apply prompt engineering, RAG, model fine-tuning, and MLOps best practices. Collaborate globally on LLM-based solutions, containerized deployments, monitoring, and event-driven AI data pipelines while following Citis development and operational standards.

Lead enterprise data modernization and AI adoption as a technical thought leader. Influence senior stakeholders, design cloud-native data and GenAI solutions (Azure, Databricks), drive MLOps/LLMOps and RAG architectures, ensure Responsible AI, mentor technical teams, and represent the company externally.

Lead product strategy and P&L for hyperscale AI connectivity products (Dark Fiber, Wave, eLine). Translate AI workload and GPU networking needs into scalable connectivity solutions, drive GTM and pricing, enable sales for large enterprise and hyperscaler deals, and align cross-functional teams on KPI-driven roadmaps.

Lead and define organization-wide Responsible AI, evaluation, and model-governance standards. Orchestrate cross-organization AI initiatives, influence engineering and executive stakeholders on architecture and roadmap, oversee end-to-end delivery, and mentor other program managers to drive strategic AI program outcomes and safety requirements.

Lead and execute audits of AI/GenAI adoption, focusing on AI governance, regulatory and ethical risks. Oversee portfolio assessments, stakeholder engagement, issue remediation, and integrated audit innovations while advising business initiatives and developing audit strategies with senior leadership.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Lead design, development, deployment, and support of enterprise intelligent automation using Power Platform and UiPath. Partner with stakeholders to identify automation opportunities, build scalable solutions, integrate with systems, deliver dashboards, enforce governance, troubleshoot complex issues, lead projects and mentor team members to drive automation CoE and continuous improvement.

Design, develop, test, operate and maintain full‑stack web applications with emphasis on frontend and backend integration. Write production-ready code, follow DevOps/CI-CD and security/compliance standards, perform code reviews, troubleshoot failures, and contribute to scalable, reliable architectures and reusable component libraries.

Design, develop, test, operate, and maintain backend AI-powered services. Produce production-ready code, ensure scalability, reliability, security, and compliance. Implement LLM/RAG solutions, integrate AI services with enterprise systems, participate in code reviews, and follow DevOps and SRE best practices while collaborating across product, architecture, and business teams.

Design, develop, test, operate, and maintain enterprise AI agents and GenAI applications. Own full-stack production code, perform code reviews, ensure security/compliance, implement RAG/vector DB solutions, and improve SDLC, CI/CD, and operational reliability.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Lead design, build, and productionize generative AI solutions (LLMs, RAG, agentic automation) for operations. Implement model engineering, deployment pipelines, AI governance/guardrails, and cloud-native, containerized infrastructure while ensuring financial services compliance and measurable impact.

Lead design, build, and operationalize generative AI solutions (chatbots, agentic automation, RAG pipelines) using open-source LLMs and cloud AI platforms. Implement model engineering, deployment, containerized cloud-native pipelines, vector databases, and AI governance/guardrails to meet financial compliance and risk requirements. Lead programs end-to-end and set technical direction for enterprise-scale AI delivery.

Design, prototype, and deploy AI-powered solutions by partnering with business stakeholders and engineering teams. Analyze processes, identify AI automation opportunities, build POCs/MVPs using LLMs and agents, define KPIs, document and train users, and drive adoption and scalability of reference AI solutions across the organization.

Lead and scale AI, ML, predictive analytics, and intelligent automation for Revenue Intelligence. Identify high-value use cases, build and deploy LLM and agentic AI solutions and production models (forecasting, propensity, recommendation, customer intelligence), drive automation, measure adoption and business value, and partner cross-functionally while managing vendors and delivery frameworks.

Build, train, deploy, and monitor propensity and predictive models to reduce churn and maximize lifetime value. Collaborate with pod-based marketing teams to enable microsegmentation and personalized customer journeys, partner with engineering to scale ML workflows, and communicate model insights to stakeholders.

Lead production support for enterprise RPA, IA, and AI programs. Serve as primary UiPath Orchestrator/Automation Cloud admin, manage L1/L2 vendors and SLAs, mentor internal staff, handle escalations, review automations for production readiness, and drive continuous improvement and stakeholder engagement.

Lead TrendAI's partner-facing product strategy for frontier AI providers and hyperscalers. Own joint roadmaps, define integration patterns, translate partner tech into product requirements, and drive co-innovation, deployments, and go-to-market with engineering, sales, alliances, and customers to deliver secure, reusable AI platform capabilities.

Lead adoption of AI-powered software engineering platforms and agentic development across engineering teams. Define AI engineering standards, pilot and scale productivity tools, design reference architectures, measure developer productivity and quality, ensure security and governance, and mentor teams on AI-assisted workflows and modern SDLC practices.

Drive Databricks adoption on Azure by leading architecture reviews, proofs of concept, and production deployments. Advise customers and partners on data, analytics, and AI architectures; resolve technical blockers; promote security, governance, reliability, and operational excellence; create reference architectures and reusable IP; and collaborate across sales, engineering, and partner teams to accelerate cloud consumption and measurable customer outcomes.

Lead strategy, roadmap, and delivery of AI-powered and digital growth marketing capabilities. Partner across Product, Data Science, Engineering, Risk, and Marketing to design, test, and scale LLM and personalization solutions, define success metrics and governance, run experimentation, and drive measurable acquisition, engagement, and efficiency outcomes.

Lead adoption of AI-assisted software engineering platforms and agentic development across engineering teams. Define standards, governance, and evaluation frameworks; design AI-native reference architectures and workflows; partner with architects, product and platform teams to pilot and scale AI coding tools; measure developer productivity and software quality; address security and SDLC modernization; mentor engineers and present strategic roadmaps to leadership.

Lead financial integrity and governance of AI initiatives for accounting, ensuring AI outputs are complete, accurate, and compliant. Manage AI governance rollouts, enforce SOX controls and data lineage, partner with technical teams on data integrity, monitor GAAP/SEC/AI regulations, and coach a technical accounting team while supporting internal systems improvements.

Lead operationalization and validation of Agentic AI and GenAI solutions: define validation frameworks, analyze production agent data, create observability dashboards, detect anomalies/hallucinations, support deployments and A/B testing, and deliver actionable insights to improve models and business outcomes.