Artificial Intelligence Jobs in Irving, TX

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Lead AI Quality Engineering initiatives for enterprise AI, establishing AI-native testing, validation, runtime quality controls, observability, and automation. Coordinate cross-functional validation, release readiness, and production reliability for LLM-enabled workflows and AI platforms. Mentor teams and drive adoption of AI-assisted testing, telemetry analysis, and continuous improvement across healthcare-related AI solutions.

Lead AI FinOps for enterprise AI platforms by managing cloud AI spend, optimizing token/GPU/inference costs, building forecasting and chargeback models, defining governance and financial guardrails, partnering with cross-functional teams, and delivering executive reporting to drive cost-effective, responsible AI adoption.

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 strategy, roadmap, and delivery for enterprise platform products enabling reusable rules, workflows, decisioning, approvals, and orchestration. Drive cross-functional alignment with engineering, architecture, AI Delivery, and security, measure platform adoption and outcomes, reduce technical debt, and build a high-performing platform product team while establishing scalable, AI-enabled product management practices.

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, 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 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.

Serve as enterprise strategic advisor on AI and automation by researching market and vendor trends, evaluating emerging capabilities, developing evidence-based adoption recommendations, supporting investment and roadmap planning, and briefing senior leaders. Maintain competitive intelligence, assess risks and governance implications, and collaborate with business, product, and governance stakeholders to align strategy and execution.

Design, build, and support an AI-ready enterprise data enablement platform using Databricks, dbt, Delta Lake, and Azure data services. Create reusable CI/CD and IaC accelerators, govern data products via Unity Catalog, enable analytical access with Starburst/Trino, troubleshoot production issues, and collaborate with cross-functional teams to deliver governed, reusable, and production-grade data products for analytics and GenAI use cases.