Artificial Intelligence Jobs in Richardson, TX

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Senior analytics leader partnering with business and finance to deliver trusted metrics, improve data quality, govern portfolio metrics, and generate decision-ready insights. Leads end-to-end analytics, drives automation and self-service, validates AI use, and communicates executive-level findings to manage lending performance, risk, and operational efficiency.

Lead second-line Compliance oversight for J.P. Morgan Wealth Management online brokerage and related products. Advise on regulatory compliance, identify and remediate conduct risks, manage compliance reviews, draft policies, support regulatory examinations, and partner with business, Legal, Risk, Technology and control functions globally.

Lead design and delivery of scalable, secure software and agentic AI engineering workflows. Architect AI-enabled SDLC automation, set governance/guardrails, build reusable frameworks, advise cross-functional teams, and influence senior leaders to safely scale agentic customer experiences.

Develop and prepare multi-dimensional semiconductor device layouts from schematics, verify data integrity and design-rule compliance, lead major block developments, mentor junior designers, improve layout efficiency via automation/AI, and collaborate across global design, verification, and CAD teams to meet schedules and deliver quality layouts.

Develop and prepare multi-dimensional semiconductor layouts from schematics, lead major block developments, verify data integrity, debug physical verification issues, mentor juniors, improve automation and layout methodologies, and coordinate with global design, verification, and CAD teams.

Lead an agile engineering team to design, develop, and operate secure, scalable software incorporating generative AI and LLMs. Drive adoption of enterprise-authorized AI-assisted development practices, evaluate vendor solutions, improve automation and reliability, and mentor engineers on responsible AI, performance, and operational stability.

Lead and execute an AI-driven automation and data strategy for global hyperscaler delivery programs. Define roadmap, prioritize use cases, lead a matrixed global team to build scalable automation tooling, enable AI/ML-driven operations, define data standards and metrics, and partner with engineering and commercial teams to industrialize and measure automation ROI.

Drive AI adoption in Payments Sales by translating sales priorities into GenAI strategy, product requirements, and data initiatives. Partner with sales, data, product, and platform teams to deliver AI-enabled tools, prioritize use cases, monitor market trends, ensure governance, and support training, adoption, and stakeholder communications to improve sales productivity and client engagement.

Lead AI strategy for Payments Sales by translating sales priorities into scalable GenAI and data-driven products, driving adoption, partnering with product/data teams, ensuring governance, and enabling sales through training and cross-functional coordination to improve productivity and revenue outcomes.

Lead oversight of AI/ML and cybersecurity operational risk across the firm: evaluate technology and cyber practices, identify threats, challenge first-line mitigations, conduct thematic analyses, assess AI/ML use cases, and collaborate with stakeholders to strengthen controls and ensure responsible AI adoption.

Lead technical contributor on an agile Investment Banking engineering team. Design, develop, debug, and optimize secure production Java/J2EE/Spring Boot systems; drive adoption and validation of enterprise-approved AI-assisted engineering practices; lead vendor evaluations, automation and remediation efforts, and communities of practice; mentor engineers on responsible AI use and ensure test automation, performance tuning, and operational stability.

Lead discovery and delivery of applied generative AI and automation for co-branded card products. Define vision, roadmap, and measurable outcomes; translate complex workflows into agent-enabled processes; partner with design, engineering, data, legal, risk, and operations; embed responsible AI controls; track KPIs; drive adoption through change management and continuous improvement.

Lead and guide agile teams to design, build, and deliver secure, scalable AI-enabled software. Develop and review production code, drive product and technical decisions, champion enterprise AI-assisted development practices, implement RAG/LLM architectures and vector stores, integrate cloud services and CI/CD, and establish responsible AI guardrails and validation standards.

Lead design, build, and support full-stack solutions (Java/Spring Boot backends, RESTful microservices, React/Angular UIs). Promote AI-assisted engineering, secure/resilient APIs, CI/CD, observability, and production support while collaborating with cross-functional teams.

Lead an agile engineering team to design, build, and deliver secure, scalable AI-enabled products. Provide technical guidance, write and review production code, drive product and architecture decisions, adopt enterprise AI-assisted development practices, implement RAG and LLM solutions, and ensure responsible AI guardrails and evaluation frameworks.

Lead AI-driven product transformation for payments: design future operating models, scale reusable AI platforms, redesign client workflows, drive adoption and responsible AI governance, define KPIs and change programs, and partner across product, engineering, operations, and risk to realize measurable outcomes.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead full-stack engineering efforts to design, build, and operate cloud-native Java/JavaScript/Python applications and scalable data pipelines. Develop and deploy AI/ML models (cloud and edge), integrate streaming systems (Kafka), manage AWS infrastructure, drive AI-assisted development practices, and provide technical leadership across architecture, code quality, and operational excellence.

Lead AI and innovation for travel products by identifying AI/ML opportunities, deploying generative AI and LLMs, managing Agile product delivery, defining epics/user stories, coordinating cross-functional teams, and measuring product metrics to improve efficiency, reduce costs, and drive revenue.

Lead and deliver secure, high-quality full‑stack Java applications; provide technical guidance, drive AI-assisted engineering practices and governance, implement REST APIs, mentor teams, and promote reusable automation across the SDLC.

Design, develop, and maintain secure, production-grade Python full‑stack applications. Produce architecture and design artifacts, troubleshoot complex systems, build data visualizations, improve code quality and resiliency, deploy containerized apps using Docker/Kubernetes on AWS EKS, and leverage AI-assisted developer tools and GenAI fundamentals for productivity.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Lead development and productionization of AI/ML solutions—focusing on LLMs and NLP—to build interactive coaching tools. Design experiments, tune models, convert prototypes into production code (Python), and integrate generative AI into ML platforms while partnering with product and business teams.