Artificial Intelligence Jobs in Irving, TX

Serve as Product Owner/Business Analyst for ServiceNow, AI, and automation initiatives supporting IT Service Desk and Field Technology Services. Elicit and document requirements, produce user stories, partner with architects/developers, support full delivery lifecycle, apply ITIL best practices, design workflows and automations, and lead stakeholder engagement and UAT.

The Engineering Manager leads the IAM Platform team, focusing on operational leadership, SRE implementation, automation, AI integration, and team development to ensure stability and innovation in identity management.

Lead product ownership for HR technology and AI: define strategy, prioritize backlog, translate requirements into epics and user stories, guide delivery and adoption, evaluate and deploy AI/LLM solutions for HR, design AI application infrastructure, and collaborate with HR, IT, architects, engineers, and data teams.

The role leads application systems analysis and programming, drives architecture strategy, evaluates technologies, and influences senior stakeholders in the investment domain.

Design, build, deploy, monitor, and optimize unattended Power Automate RPA workflows. Collaborate with business and IT partners, ensure compliance and security, document solutions, perform testing and code reviews, support production, and cross-train on Automation Anywhere.

The Global Financial Crimes Analyst evaluates and implements technologies to enhance compliance and risk detection in financial crimes. Responsibilities include collaborating with functional teams, developing automation solutions, analyzing vendors, and supporting compliance initiatives.

Responsible for building conversational AI experiences using Dialogflow CX, integrating backend systems, conducting QA, and leading teams on AI initiatives.

The Cyber AI Senior Specialty Software Engineer will design, develop, and deploy AI applications while integrating them with existing systems and resolving technical issues.

Lead the design and development of AI solutions and MCP servers, focusing on AWS infrastructure and Python development. Responsibilities include automating processes, managing deployments, and conducting architectural governance.

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 audits of AI, data and digital risk frameworks; assess AI governance, identify gaps and risks, recommend improvements, evaluate third-party AI vendors, and produce actionable audit reports for senior leadership and regulators.

As a Lead Software Engineer, you will drive technical initiatives to develop the AskGartner AI platform, manage architecture decisions, mentor engineers, and collaborate across teams to deliver scalable solutions.

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.

The Senior Generative AI Developer will design, develop, and implement generative AI solutions, optimize LLMs, ensure robust deployments, and mentor junior team members while adhering to ethical AI standards.

Support the Head of AI and Business Execution Lead to drive AI strategy execution, prepare executive-level materials, coordinate cross-functional initiatives, monitor progress, manage stakeholders, and apply an AI-first, risk-aware approach to deliver strategic outcomes and governance across Citi.

As a Software Engineer, you'll design AI applications, implement multi-agent architectures, build RAG pipelines, and ensure enterprise-grade reliability by optimizing performance and collaborating with teams.

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 the development of AI applications and frameworks, ensuring adherence to system architecture and industry standards. Collaborate with management to enhance system functionalities and deploy new products while mentoring mid-level developers.

Design and implement enterprise-grade LLM-powered AI systems, ensuring compliance with risk, governance and regulatory standards, while providing technical leadership.

Lead an AI archetype portfolio from discovery to production, define product strategy and roadmaps, partner with Technology and Risk, ensure compliant delivery in a regulated environment, drive adoption and measurable business value, and promote reusable AI capabilities and scalable platforms.

Lead enterprise AI platform strategy and implementation across the bank. Build and manage multidisciplinary teams, establish AI governance, mitigate bias/privacy risks, integrate AI into core systems, collaborate with risk/legal/compliance, drive platform delivery, upskill staff, and track AI performance metrics for business impact.

Build and maintain Python-based services that integrate Generative AI into enterprise data and ML platforms. Design, implement, and scale cloud-hosted solutions, mentor junior developers, ensure governance and security compliance, and evaluate emerging AI technologies to improve developer productivity and business value.

Lead strategy and execution for Generative AI initiatives: build and mentor a Gen AI team, architect LLM-based solutions, integrate vector/graph databases and speech-to-text, apply advanced retrieval techniques, collaborate cross-functionally, and drive adoption and ROI through continual research and stakeholder communication.

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 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.