Artificial Intelligence Jobs in Irving, TX

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.

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.

Build, optimize, and scale production Generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and deploy LLMs, and create high-performance FastAPI async backends. Improve latency, cost, and reliability, containerize services with Docker, and manage monitoring, API security, and CI/CD for ML deployments.

Design, build, and deploy production-ready generative AI applications: architect multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deliver scalable FastAPI-based services with monitoring, CI/CD, and containerized deployment.

Design, build, and productionize generative AI solutions: advanced Python development, LLM prompt engineering and fine-tuning, agent frameworks, RAG pipelines with vector DBs, API and cloud deployment, MLOps/LLMOps, model evaluation and Responsible AI practices, and translate business needs into scalable AI applications.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

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.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, integrate and fine-tune various LLMs, and deploy containerized FastAPI services with monitoring, CI/CD, and performance/cost optimizations.

Develop, analyze, and implement application systems using Python and generative AI frameworks. Build and maintain RESTful APIs, cloud-deployed containerized services, and ensure security, testing, and operational support. Consult with stakeholders, perform feasibility and post-implementation analysis, mentor junior analysts, and drive compliance with policies and risk controls.

Lead strategy and delivery of digital workplace infrastructure products to improve employee experience. Use DEM/DEX telemetry and analytics to prioritize backlogs, define requirements, and drive automation, self-service, and operational efficiency. Partner with engineering, operations, and support to monitor KPIs, resolve issues, and continuously improve product outcomes using agile practices.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.

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 the development of an intelligent document processing platform using Generative AI, managing a high-performing engineering team, and driving technical innovation.

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 design and development of scalable full-stack applications using Java, Angular, microservices, and event-driven architectures. Integrate and deploy agentic AI/GenAI/LLM solutions for M&A use cases, architect resilient systems, implement CI/CD and cloud/container platforms, promote engineering best practices, and mentor teams across the SDLC while partnering with Architecture, Security, Risk, and Business stakeholders.

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 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.

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 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.

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.