Artificial Intelligence Jobs in Plano, TX

Supports AI, machine learning, and generative AI product initiatives for travel products. Responsibilities include identifying opportunities, developing requirements and user stories, coordinating cross-functional delivery, tracking projects with Agile tools, analyzing product metrics, conducting user and market research, and communicating AI-driven product impacts to stakeholders.

Design, develop, test, and maintain scalable data pipelines and architectures. Ensure data quality, lineage, governance, and security. Build automated testing/monitoring, optimize storage and query performance, update data models, and support data integration using SQL/NoSQL, cloud, and distributed processing frameworks.

Leads AI and automation strategy for Ericsson’s Cloud Software Services organization. Manages the AI use-case funnel, value tracking, governance, architecture alignment, automation adoption, and scaled deployment of high-impact solutions. Drives cross-functional collaboration, hackathons, knowledge sharing, and continuous improvement across business, finance, sourcing, and operations teams. Serves as the primary representative in organizational AI forums and provides executive-ready reporting.

Build and deploy production-grade AI, generative AI, and agentic automation solutions for telecom operations. Responsibilities include developing Python REST APIs, orchestrating LangChain and LangGraph agents, creating SQL and Pandas data pipelines, integrating enterprise APIs, and deploying cloud-native applications with Docker, Kubernetes, and Azure. The role also involves monitoring, troubleshooting, documenting architectures, and partnering cross-functionally to identify efficiency and cost-reduction opportunities.

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 cybersecurity strategy and architecture for AI-enabled products across the CCB portfolio. Design reusable cloud-native security frameworks, perform threat modeling and secure design, guide adoption of modern security methods (A2A, API, service mesh, certificate management), and influence cross-functional teams to implement robust, scalable cybersecurity architectures.

Intern will develop Python automation for cloud rApp projects, use Ericsson GenAI tools to build agent-based automation, create/validate cloud config JSON/YAML, support non-functional testing (Robot Framework, JMeter/LoadRunner), document processes, and collaborate with the team on cloud automation and performance test scenarios.

Lead development and delivery of secure, production-grade AI-enabled applications. Design, code, test, and operate AI features; drive adoption of AI-assisted engineering practices; evaluate model safety and integrations; automate remediation and reliability; coach teams on responsible AI, secure coding, and CI/CD for cloud-native environments.

Lead design, develop, and deliver secure, scalable production software using Java/Python and cloud (AWS). Drive AI-assisted development adoption, evaluate vendors, automate remediation, mentor teams, and ensure CI/CD, resiliency, and security best practices.

Design, build, and operate agentic LLM toolchains to ingest mainframe artifacts, generate verified modern service specifications, translate code to Kotlin/JVM, implement RAG and evaluation pipelines, and run LLMOps for production reliability and migration parity testing.

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 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 design and build of agentic AI systems and production services. Produce secure, high-quality code and architectures, drive AI-assisted engineering practices, ensure observability, testing, compliance, and collaborate cross-functionally to deliver scalable, reliable agentic solutions.

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 development of state-of-the-art ML and deep learning solutions for NLP, speech analytics, time series, reinforcement learning and recommendation systems. Collaborate with business, product, legal, compliance and engineering teams to deploy scalable models into production, design experiments and metrics, and coach AI/ML team members.

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.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead and deliver secure, scalable Java/Spring enterprise applications; drive adoption of enterprise AI-assisted engineering practices; design, develop, review, and stabilize production code; evaluate vendors and lead engineering communities of practice.

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 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 end-to-end development and deployment of ML/AI models for consumer finance products, partner with business and governance, ensure regulatory compliance, monitor performance, and mentor junior team members.

Lead design and develop agentic AI systems and production services using Java/Python and Spring or Python web frameworks. Build secure, scalable production code, APIs, and CI/CD pipelines; apply LLM patterns (RAG, function calling), observability, testing, and responsible AI practices. Mentor teams, drive adoption of enterprise AI-assisted engineering, and ensure compliance, reliability, and operational excellence.

Leads USAA’s enterprise Concentration of Risk framework, using actuarial, catastrophe, climate, statistical, and predictive analytics to identify risk concentrations and guide pricing, underwriting, growth, profitability, and capital decisions. Partners with cross-functional teams and executives, develops risk metrics and mitigation strategies, governs enterprise reporting, manages vendors, and communicates complex recommendations to senior stakeholders.

Lead vision, strategy, roadmap, innovation, pricing, and lifecycle management for USAA deposit products, overdraft services, and related fees. Analyze performance, market trends, profitability, and regulatory requirements to guide product decisions, forecasts, launches, enhancements, and go-to-market strategies. Partner with marketing, finance, legal, compliance, risk, credit, and operations while presenting recommendations to executive leadership and managing product risks.

Design, develop, and troubleshoot secure, scalable production code and ML platforms. Drive enterprise adoption of AI-assisted engineering practices, automate remediation, evaluate vendor solutions, apply SDLC/CI-CD, and ensure responsible AI, data sensitivity, resiliency, and operational stability.