Artificial Intelligence Jobs in Plano, TX

Provides cybersecurity consulting and governance for a business function, evaluating architectures, infrastructure, cloud, network, data, access, and resilience risks. Advises engineering and product teams on security capabilities, risk mitigation, and secure development. Manages cyber risks, supports incident analysis, presents security health to executives, and evaluates AI-related security, privacy, compliance, and attack risks.

Develop and execute indirect procurement category strategies for capital equipment, operational equipment, and complex services. Lead strategic sourcing, RFx initiatives, cost reduction, should-cost analysis, contract negotiations, supplier relationship management, and performance improvement. Partner with Legal, Global Procurement, executives, and cross-functional stakeholders while managing commercial and operational issues. The role requires data-driven recommendations, multiple concurrent sourcing projects, and 15% domestic travel.

Serve as a senior Second Line of Defense cybersecurity risk partner, independently assessing cybersecurity controls, secure development practices, cryptographic strategies, AI governance, and technology risks. Provide risk opinions, challenge risk acceptance decisions, recommend remediation, and enhance cybersecurity frameworks, standards, policies, and governance. Engage with technical and executive stakeholders, monitor emerging threats and regulatory expectations, and support research and testing across a complex financial services environment.

Lead the design and implementation of Generative AI solutions for complex business challenges. Collaborate with data scientists, engineers, and product managers; conduct research; build prototypes; demonstrate concepts; oversee data processing and model development; and support implementation and optimization. Translate stakeholder needs into technical requirements, manage cross-functional relationships, communicate AI concepts, and apply project management practices to estimate timelines and coordinate dependencies.

Owns the Trade and Working Capital AI use-case portfolio from ideation through delivery and value realization. Translates business needs into technical requirements, prioritizes opportunities, coordinates with AI, data science, technology, data, and infrastructure teams, and drives adoption across product, commercial, and productivity applications. The role also leads AI literacy initiatives, aligns business priorities with enterprise governance and AI programs, manages multi-workstream delivery, and reports progress and realized value to senior stakeholders.

Leads Card customer analytics and AI strategy, overseeing analytics leaders, data scientists, and analysts. Drives GenAI, machine learning, experimentation, personalization, competitive intelligence, and forward-looking analysis across product, pricing, marketing, and servicing. Partners with business and technical stakeholders to deliver responsible solutions from concept through production, while building organizational capabilities, developing talent, and influencing executive decisions.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

Leads a data science team within wealth management, delivering analytics, machine learning, experimentation, and AI solutions that improve client outcomes, product performance, and operational efficiency. Partners with product, strategy, and operations leaders to define problems, metrics, and recommendations. Oversees modeling, client behavior analysis, experimentation frameworks, executive communication, and AI use-case development while coaching team members and managing performance.

Leads payments governance and third-party oversight, including risk assessment, control evaluation, due diligence, monitoring, issue remediation, and regulatory compliance. Manages a multidisciplinary team, modernizes governance through automation and analytics, directs transformation programs, and advises senior stakeholders on risk posture, escalations, and readiness decisions across the payments ecosystem.

Lead end-to-end delivery of cloud-native, full-stack software initiatives, defining architecture, engineering standards, reliability practices, observability, CI/CD, and secure production operations. Build microservices, APIs, and AI capabilities using LLMs and generative AI. Guide responsible AI-assisted development, lead incident response and continuous improvement, mentor engineers, and align technical priorities across stakeholders and initiatives.

Leads development of full-stack, cloud-native software using Java, Python, and AWS. Builds secure microservices, APIs, and LLM-powered capabilities; establishes engineering, testing, reliability, observability, and CI/CD standards; mentors engineers; drives responsible AI-assisted development; and manages production operations, incident response, and technical direction across agile initiatives.

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.

Designs and scales experiential learning programs that build employee AI fluency across roles and regions. Responsibilities include needs analysis, curriculum and journey design, tool-specific training for generative AI and Microsoft Copilot, content QA, learner testing, stakeholder collaboration, measurement planning, benchmarking, and responsible AI education. The role partners with HR, technology, analytics, compliance, product, and business teams to drive AI adoption in a regulated enterprise environment.

Lead Ericsson’s enterprise AI strategy and portfolio across HR, Finance, Sourcing, Customer Support, Marketing, Product Development, Network, and Cloud. Translate business challenges into AI roadmaps, solution architectures, investment cases, and measurable outcomes. Oversee initiatives from opportunity identification through delivery, adoption, and value realization. Prioritize data and AI investments, model ROI and TCO, evaluate vendors and platforms, and advise senior stakeholders on scalable, secure, governed, and commercially viable solutions.

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.

Lead development of an AI agent platform using Java, Python, AWS, and React. Build cloud-native services, APIs, integrations, guardrails, observability, CI/CD automation, and production operations. Mentor engineers, lead design and code reviews, promote responsible AI-assisted development, and resolve performance and reliability issues.

Leads design and development of scalable agentic AI solutions using Java and/or Python. Builds LLM-powered systems, MCP servers, APIs, production services, and cloud infrastructure on AWS. Establishes secure coding, testing, observability, reliability, and responsible AI practices. Guides team adoption of approved AI-assisted development tools, validates AI-generated outputs, and collaborates across product, design, and operations teams to deliver compliant enterprise applications.

Leads cybersecurity architecture strategy and solutions across cloud-based applications and technical domains. Analyzes risk, designs controls, evaluates emerging technologies, and drives automation. Develops security analytics dashboards, data models, pipelines, alerts, and executive reporting while ensuring data quality, traceability, resiliency, and auditability. Partners with vendors, technical teams, and senior business leaders to improve cybersecurity outcomes.

Develop secure, scalable full-stack data platforms and applied AI capabilities within an agile team. Build agentic applications, AI agents, MCP servers, and LLM-powered features using RAG, embeddings, evaluation, guardrails, and tool calling. Implement frontend and backend services, data engineering pipelines, cloud-native platform capabilities, CI/CD, observability, and controlled releases. Analyze large datasets, troubleshoot technical issues, and validate AI-assisted development outputs through testing, peer review, and secure coding practices.

Build and operate production platforms for LLM-powered agents and AI features. Responsibilities include developing reusable services, evaluating and testing LLM systems, implementing observability, applying security and governance controls, and improving AI-assisted engineering practices. The role partners with product, engineering, data, security, and compliance teams to deliver scalable solutions, with preferred experience in orchestration frameworks, RAG, vector databases, Docker, Kubernetes, AWS, and Databricks.

Leads third-party cyber and technology risk assurance, including strategy, governance, control assessment, remediation, regulatory alignment, reporting, and risk analytics. Partners with control owners, procurement, legal, security, and business stakeholders to strengthen third-party controls and contractual protections. Uses authorized AI to synthesize evidence and support executive reporting while validating outputs for security, auditability, and regulatory compliance. Drives continuous improvement and manages complex cross-functional initiatives.

Leads enterprise cloud platform adoption across business lines by advising engineering teams on architecture, onboarding, migration, governance, networking, and identity. Builds automation and self-service tooling, develops documentation and training, evaluates infrastructure technologies, and translates customer feedback into platform improvements. Applies enterprise-authorized AI responsibly within engineering workflows while validating recommendations for resiliency, security, auditability, and data sensitivity.

Lead architecture strategy and roadmaps for cloud-based, AI-enabled payment systems. Advise on technology selection, build reusable secure engineering patterns, develop and review production code, govern APIs and integration patterns, and drive digital transformation for onboarding and self-service experiences.

Lead enterprise technology change and communications initiatives, developing strategies, executive messaging, stakeholder engagement, communication programs, governance frameworks, and effectiveness metrics. Partner with senior leaders and cross-functional teams to support transformation initiatives, manage issues and escalations, ensure organizational alignment, and communicate program outcomes. Use authorized AI capabilities to assist with planning, risk synthesis, reporting, and governance while validating outputs and protecting sensitive data.

Own the vision, strategy, roadmap, backlog, metrics, and end-to-end delivery of enterprise contact center capabilities. Integrate Amazon Connect workflows with business applications, lead discovery and stakeholder alignment, manage product readiness and adoption, and ensure secure, reliable, measurable outcomes across engineering, operations, risk, and business teams.