Artificial Intelligence Jobs in Irving, TX

Lead product definition and delivery of AI-driven capabilities for secure network services. Own backlog, translate business needs into user stories, prioritize features, partner with engineering and governance teams, define KPIs, validate model effectiveness, and ensure compliance with security and responsible AI principles to improve network performance, reliability, and operational efficiency.

Lead technical strategy and hands-on implementation of scalable, cloud-native data ingestion and processing platforms for sensor and telemetry data (LiDAR, radar, video, imagery, vehicle telemetry). Drive architecture, streaming and batch pipelines, SDLC/CI-CD, observability, and cross-team agile delivery to enable ML, autonomy, and analytics at enterprise scale.

The Engineering Manager leads the IAM Platform team, focusing on operational leadership, SRE implementation, automation, AI integration, and team development to ensure stability and innovation in identity management.

Design, deploy, and operationalize production AI/LLM solutions (RAG, agents, vector DBs) to detect and monitor enterprise risks. Build prompt libraries, AI governance, APIs, dashboards and visualizations. Lead risk analytics, translate insights into executive reporting, advise stakeholders, and mentor analysts to scale AI-driven risk monitoring and remediation.

The role leads application systems analysis and programming, drives architecture strategy, evaluates technologies, and influences senior stakeholders in the investment domain.

The Global Financial Crimes Analyst evaluates and implements technologies to enhance compliance and risk detection in financial crimes. Responsibilities include collaborating with functional teams, developing automation solutions, analyzing vendors, and supporting compliance initiatives.

Responsible for building conversational AI experiences using Dialogflow CX, integrating backend systems, conducting QA, and leading teams on AI initiatives.

The Cyber AI Senior Specialty Software Engineer will design, develop, and deploy AI applications while integrating them with existing systems and resolving technical issues.

Design, build, deploy, monitor, and optimize unattended Power Automate RPA workflows. Collaborate with business and IT partners, ensure compliance and security, document solutions, perform testing and code reviews, support production, and cross-train on Automation Anywhere.

The Senior Generative AI Developer will design, develop, and implement generative AI solutions, optimize LLMs, ensure robust deployments, and mentor junior team members while adhering to ethical AI standards.

Conduct original AI research and experiments focused on generative AI, LLMs, multimodal and agentic systems; rapidly prototype and productionize solutions; design evaluations, RAG and fine-tuning workflows; collaborate with engineering and product teams to deploy scalable, secure, and compliant AI capabilities across the bank.

Design, build, and scale enterprise-grade LLM and agent-based systems (using ADK, LangChain). Implement multi-agent orchestration, guardrails, API integrations, real-time/asynchronous pipelines, observability, and partner with Risk/Compliance for MRM validation while leading delivery and mentoring engineering teams.

Drive Databricks adoption on Azure by leading architecture reviews, proofs of concept, and production deployments. Advise customers and partners on data, analytics, and AI architectures; resolve technical blockers; promote security, governance, reliability, and operational excellence; create reference architectures and reusable IP; and collaborate across sales, engineering, and partner teams to accelerate cloud consumption and measurable customer outcomes.

Lead enterprise AI platform strategy and implementation across the bank. Build and manage multidisciplinary teams, establish AI governance, mitigate bias/privacy risks, integrate AI into core systems, collaborate with risk/legal/compliance, drive platform delivery, upskill staff, and track AI performance metrics for business impact.

Lead AI research and innovation focused on LLMs, generative and agentic systems; prototype and productionize models; design experiments, evaluate results, and ensure explainability, security, and compliance; mentor teams and drive enterprise AI adoption across financial services.

Provide strategic legal counsel on procurement, development, deployment, and commercialization of technology and AI solutions. Draft, review, and negotiate complex technology agreements; advise on AI, data use, privacy, HIPAA, cybersecurity, and IP; develop AI governance, contracting standards, and scalable legal processes.

Design, build, and deploy AI-driven automation pipelines using workflow orchestration and agent frameworks to replace manual workflows. Integrate LLMs, APIs, and internal systems, ensure logging, security, and observability, and collaborate cross-functionally to productionize and maintain reliable automation solutions.

Lead development and execution of AI-integrated content strategies for financial products. Architect language systems, information architectures, and CMS/AI tooling; ensure content accuracy, brand alignment, ethical standards, and optimized digital engagement. Collaborate with product, design, and engineering teams, apply analytics and UX/journey thinking, and continuously refine AI content capabilities.

Support the Head of AI and Business Execution Lead to drive AI strategy execution, prepare executive-level materials, coordinate cross-functional initiatives, monitor progress, manage stakeholders, and apply an AI-first, risk-aware approach to deliver strategic outcomes and governance across Citi.

Lead design and implementation of Java/Spring middleware and microservices, optimize Oracle/SQL and MongoDB interactions, integrate AI tools into development and CI/CD, mentor engineers, drive architecture, testing, deployment, and production stability, and apply AI for code generation, monitoring, and risk analysis.

Lead cloud and data platform engineering for analytics and AI: implement DevSecOps CI/CD pipelines, automate infrastructure with IaC, manage Snowflake data warehouse and ingestion, oversee cloud cost reporting and FinOps, and evaluate/operate emerging AI/LLM capabilities and governance. Act as senior individual contributor collaborating with security, finance, and engineering teams to optimize performance, security, and cost.

Lead technical partner for WCIB AI adoption: identify and prioritize AI use cases, design scalable solutions, lead delivery from concept to production, provide architecture and engineering leadership, align with enterprise AI teams, present solutions to stakeholders, and mentor/scale engineering capability.

As a Software Engineer, you'll design AI applications, implement multi-agent architectures, build RAG pipelines, and ensure enterprise-grade reliability by optimizing performance and collaborating with teams.

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.