Artificial Intelligence Jobs in Plano, TX

Lead design and deliver AI-enabled data platform capabilities (metadata, lineage, data quality, retention). Drive adoption of enterprise-authorized AI-assisted engineering, build agentic production systems, develop secure production code, automate remediation, establish production SRE controls, and mentor teams on responsible AI and validation standards.

Leads Cisco’s Product Legal teams supporting Security, Observability, and Data Platform business units. Advises senior executives on product development, technology strategy, go-to-market models, intellectual property, licensing, privacy, AI, cybersecurity, regulatory requirements, and technology transactions. Oversees legal support throughout product lifecycles, develops scalable legal strategies, manages organizational integration, and builds high-performing legal teams across global technology portfolios.

Lead design and deliver cloud-native microservices and AI/LLM-driven features. Architect scalable, secure systems, drive AI-assisted engineering practices, improve observability/automation, mentor teams, and integrate generative AI into business workflows.

Lead hands-on engineer designing and modernizing cloud-native, reliable, and automated software solutions. Drive adoption of enterprise AI-assisted engineering practices, build automation frameworks, support performance and resiliency testing, lead code reviews, and communicate technical strategy and reliability standards across teams.

Summer Analyst in Data & AI will design and build end-to-end data, analytics, and ML/AI solutions using cloud and modern tooling. Responsibilities include developing models and pipelines, running experiments tied to business KPIs, supporting data governance and compliance, creating dashboards, and collaborating in agile teams while receiving mentorship and training.

Advise and advance enterprise Third Party Risk Management capabilities, policies, governance, and operating practices. Assess complex third- and fourth-party risks, develop enterprise risk strategies, lead cross-functional initiatives, advise senior leaders, improve governance and analytics, and influence stakeholders across technology, cybersecurity, procurement, legal, resilience, and risk functions.

Build rapid AI prototypes, applications, point solutions, and agentic workflows alongside client engagement teams. Translate business challenges into working solutions, validate feasibility, align stakeholders, and hand off prototypes to Product and Technology. Guide teams in adopting AI-first approaches using approved platforms, controls, and guardrails. Capture business logic, support change management, connect clients with AI product teams, and teach technical practices through hands-on collaboration.

Supports AI-first process improvement and transformation engagements by analyzing processes and data, building prototypes and agentic workflows, documenting business logic, developing recommendations, and communicating solutions to technical and non-technical stakeholders. Works under senior guidance while developing technical credibility, applying generative AI responsibly, considering adoption and change management, and delivering assigned workstream outcomes. Travel may be required up to 10%.

Lead a team and portfolio building full-stack, cloud-native microservices and platforms. Collaborate with product managers to deliver scalable AWS solutions, mentor engineers, experiment with new technologies, and ensure regulatory needs are met using languages like JavaScript, Java, TypeScript, Python, Go, and container orchestration (Docker, Kubernetes).

Leads the design, development, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with research, engineering, product, and program teams; contributes to technical vision; and mentors engineers while applying emerging AI research in production.

Designs, develops, reviews, and troubleshoots secure production software. Delivers Python-based solutions on AWS and Snowflake, drives automation and operational stability, leads engineering best practices, uses AI-assisted development tools responsibly, and contributes to product vision and team culture.

Leads software engineering projects and developers building distributed microservices, full-stack systems, and cloud-based solutions. Collaborates with product managers, addresses regulatory needs, evaluates emerging technologies, mentors engineers, and contributes to technology communities. The role uses languages including Python, Java, JavaScript, TypeScript, SQL, and Go, along with AWS, containers, Kubernetes, relational databases, NoSQL, Snowflake, Databricks, and AI tooling.

Leads development of resilient, scalable Java and Python services, enterprise integrations, and AI-assisted engineering practices. Architects microservices, Kubernetes deployments, messaging, observability, and backend infrastructure. Builds ML pipeline and feature-store capabilities for fraud and risk models, operationalizes models, and creates agentic AI services with production guardrails, monitoring, governance, and auditability. Mentors engineers, coordinates delivery across stakeholders, and ensures security, performance, testing, and operational requirements.

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.

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.

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.

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.

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.

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.

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.