Artificial Intelligence Jobs in Irving, TX

Serves as the Deposits, Liquidity, and Pricing & Rates data domain expert. Defines business data standards, quality controls, metadata, lineage, governance, and AI readiness. Partners with AI, engineering, product, and governance teams to support forecasting, funding, account visibility, and pricing use cases. Advises leadership, resolves complex data issues, translates technical concepts into business insights, and leads cross-functional decision-making and execution.

Serves as the technical authority for MineStar Onboard autonomy software on mining machines. Decomposes site designs into system architectures and requirements, manages interfaces and traceability, leads design reviews and trade studies, and supports integration, testing, verification, certification, and release readiness. Provides technical guidance on geospatial data, secure communications, deployment, escalated issues, and system-level risks while collaborating with product teams in a SAFe Agile environment.

Own the vision, strategy, roadmap, backlog, delivery, adoption, and outcomes for an AI-powered Experience Optimization Agent. Lead experimentation and personalization capabilities integrating Adobe Target, AI recommendations, automation, and guided workflows. Partner with engineering, architecture, data, analytics, design, marketing, risk, legal, and compliance teams to deliver enterprise-scale platform capabilities while embedding governance, privacy, security, responsible AI, and human oversight.

Leads enterprise digital strategy and planning across complex product portfolios. Advises senior leadership on product direction, investment decisions, priorities, roadmaps, and long-term goals. Synthesizes customer insights, market trends, performance data, and platform considerations into executive recommendations and requirements. Coordinates cross-functional initiatives involving product, design, engineering, data, analytics, operations, risk, and control teams while influencing decisions without direct authority.

Leads architecture, development, testing, validation, certification, and release of embedded software for robotic, autonomous, and safety-critical machines. Owns integration and build pipelines, develops operator and machine safety systems, evaluates AI and machine learning technologies, advises on complex integrations, and mentors engineers. The role requires extensive platform-level software development experience, robotics and autonomy expertise, an engineering or computer science degree, full-time onsite work in Irving, Texas, and up to 10% travel.

Leads execution and adoption of Real-Time Agent Assist capabilities, translating product direction into plans and measurable outcomes. Coordinates Product, Technology, Operations, Risk, Compliance, and Change Management stakeholders; manages dependencies and business readiness; evaluates AI use cases; defines requirements for performance, explainability, oversight, and responsible AI; and uses performance insights and user feedback to drive continuous improvement.

Leads a multidisciplinary data science and AI pod focused on AI-assisted engineering tools, adoption, analytics, and productivity measurement. Develops technical plans, guides deployment and troubleshooting, supports Agile product ownership, reports actionable insights, and mentors data science, machine learning, GenAI, and software engineering professionals. Partners with users, product owners, architects, engineers, and business stakeholders to shape scalable enterprise AI enablement services while applying responsible AI principles.

Lead robotics engineering efforts across perception, planning, control, and learning for autonomous off-road machines. Design, test, certify, and deploy production-quality autonomy components on real hardware, taking systems from prototype through production. Collaborate across the full robotics stack while specializing in AI, motion planning, perception, controls, or SLAM. Work includes simulation, vehicle deployment, field robotics, and potentially MLOps and training infrastructure.

Leads digital architecture for autonomous robotics and machine platforms. Defines technical standards, architecture patterns, and autonomy roadmaps spanning robotics, controls, machine integration, onboard computing, AI, and perception. Directs rapid prototyping, proof-of-concepts, architecture projects, and emerging technology evaluation while advising engineering teams and resolving complex technical issues. The role also establishes processes, influences stakeholders, and supports scalable deployment of safe, reliable autonomy capabilities across industrial machine platforms.

The Principal Robotics Engineer will design, develop, test, and deploy autonomy components across perception, planning, control, and learning for large off-road machines. The role owns robotics components from prototype through production on real hardware, contributes across the autonomy stack, and participates in software testing, analysis, witnessing, and certification. Responsibilities include field deployment, simulation, on-vehicle development, and collaboration within outcome-focused engineering teams.

Design, build, test, certify, and deploy autonomy components across perception, planning, control, and learning for large off-road machines. Own robotics components from prototype through production hardware, contribute across the autonomy stack, and support simulation, on-vehicle deployment, and real-world field operations.

Leads AI product area strategy, roadmap ownership, backlog prioritization, capacity planning, and business adoption initiatives. Analyzes complex products and regulatory impacts, manages risk, drives technology process improvements, and collaborates with cross-functional stakeholders to deliver product enhancements. The role also leads projects, mentors staff, and supports AI readiness within a regulated banking environment.

Designs and optimizes AI-powered workflows using LLMs, agents, prompts, tools, context, and orchestration frameworks. Translates business challenges into scalable multi-step workflows, integrates solutions with enterprise applications, troubleshoots workflow and agent issues, analyzes performance data, develops automation utilities, and supports AI governance, risk management, responsible AI, monitoring, and quality assurance.

Lead AI product strategy, adoption, roadmap prioritization, backlog enhancements, and business-change initiatives within a regulated banking environment. Analyze AI usage, feedback, and outcomes; optimize prompts, workflows, user experience, and business rules; validate performance through testing and quality assurance; manage risk and governance; and report on business benefits. Partner with operations, technology, product, and business stakeholders to deliver improvements and drive adoption.

Owns the architecture of Caterpillar’s reinforcement learning systems for physical AI and autonomous construction equipment. Designs simulation-based training, imitation learning, foundation-model post-training, sim-to-real transfer, policy evaluation, safety, and deployment methodologies. Establishes standards for reproducibility, experimentation, benchmarks, and field performance while guiding cross-functional teams and translating research into reliable, production-ready learning systems.

Leads analytics and AI strategy, delivery, governance, and platform ownership for internal auditing. Manages data scientists, develops Power BI and Snowflake solutions, oversees reporting and control monitoring, and drives automation and AI adoption. Partners with audit and business leaders to translate requirements into scalable insights, improve decision-making, and establish secure, reliable analytics practices. The role also manages stakeholder relationships, vendor partnerships, team development, and analytics transformation initiatives.

Leads enterprise testing strategy and governance for AI-powered contact center and CRM solutions. Oversees UAT, validation, release readiness, test automation, defect management, metrics, controls, and operational adoption. Coordinates cross-functional stakeholders, SMEs, analysts, and resources while improving testing practices and offshore capacity. The role requires expertise in conversational AI, telephony, omnichannel routing, speech analytics, integrations, Agile delivery, and test management tools.

Design, develop, test, certify, and deploy autonomy components for large off-road machines across perception, planning, control, and learning. Take robotics solutions from prototype through production on real hardware, contribute across the autonomy stack, and collaborate in outcome-focused engineering pods. The role involves simulation, on-vehicle deployment, field robotics, and potentially machine-learning infrastructure.

Leads hands-on development and architectural design of scalable, secure middleware platforms. The role establishes engineering standards, mentors engineers, applies DevSecOps and SRE practices, advises business and product stakeholders, and drives delivery of complex software solutions. Responsibilities include building Java and Spring Boot systems using Kafka, microservices, GraphQL, and NoSQL technologies; improving resilience and operational excellence; evaluating emerging technologies; and ensuring high-quality design, code, security, and technical documentation.

Researches digital workplace infrastructure and operations, develops market insights, advises senior IT executives, analyzes vendors and operating models, presents recommendations, supports sales and client engagements, and mentors analysts. Focus areas include AI-driven automation, endpoint management, DaaS, VDI, digital employee experience, productivity, and modern end-user services.

Lead Citi's US Consumer Cards technology strategy and modernization program. Define multi-year roadmaps for legacy credit card platforms, transition architectures, integration blueprints, governance milestones, and success metrics. Build economic models covering total cost of ownership, infrastructure optimization, and engineering productivity. Translate technical and financial analysis into executive presentations, establish benefit-realization scorecards, and advise senior stakeholders on modern payments architecture and cloud-native transformation.

Defines data architecture for physical AI and autonomous systems, including data contracts, schemas, metadata, governance, lineage, and traceability. Designs multimodal architectures for sensor, video, image, telemetry, event, simulation, and robotics data. Establishes scalable data patterns for robotics training, synthetic data, world models, and AI development. Leads architectural roadmaps and standards while partnering with simulation, robotics, data engineering, and platform teams.

Leads Vizient’s indirect spend data and analytics strategy, operating model, governance, quality standards, reporting, automation, and AI adoption. Oversees data lifecycle management, modeling, integration, categorization, normalization, analytical methodologies, dashboards, and decision-support tools. Manages data and analytics teams, develops reusable assets for consulting engagements, partners with technology and product teams, and supports business development and member presentations.

Leads enterprise cybersecurity and infrastructure programs spanning vulnerability management, patching, network security modernization, operational resilience, technology lifecycle management, and regulatory compliance. Establishes governance, roadmaps, budgets, executive reporting, and risk-reduction processes while coordinating Cybersecurity, Infrastructure, Engineering, Cloud, Risk, Compliance, and Operations stakeholders. Oversees remediation, security transformation, portfolio financials, vendor management, and adoption of automation, AI, and analytics. Influences senior executives and drives outcomes across complex, regulated environments.

Defines enterprise data and AI infrastructure architecture for physical AI and autonomous systems. Designs data domains, canonical models, data products, GPU platforms, distributed training environments, simulation pipelines, MLOps workflows, and edge deployment architectures. Leads technology evaluation, technical standards, implementation planning, and cross-functional delivery across cloud and on-premises environments. Enables engineers and data scientists to develop, validate, and deploy scalable AI models for autonomous construction equipment.