Machine Learning Jobs in Irving, TX

Lead a team of propensity modelers, deploy data scientists into cross-functional pods, partner with marketing to reduce churn via ML-driven personalization, manage chapter operations (hiring, training, resources), establish modeling governance and standards, drive cross-functional collaboration, and present AI strategies to executives.

Entry-level Associate Data Analyst supporting analytics, data science, data engineering, and project management rotations. Deliver insights, build dashboards, automate reporting, partner with stakeholders, and apply ML/LLM tools to drive product and client analytics.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.

Lead test strategy, design test cases, and build automated test scripts and frameworks for enterprise applications. Execute manual and automated testing (functional, integration, performance, security), data/ETL validation, and defect analysis. Implement AI-driven and agentic automation solutions, mentor team members, collaborate with cross-functional teams, and continuously improve QA processes and tooling.

Lead enterprise data modernization and AI adoption as a technical thought leader. Influence senior stakeholders, design cloud-native data and GenAI solutions (Azure, Databricks), drive MLOps/LLMOps and RAG architectures, ensure Responsible AI, mentor technical teams, and represent the company externally.

Lead MarTech strategy and hands-on execution for AI-driven, real-time 1:1 personalization. Evolve and evaluate MarTech stack, integrate AI/ML decisioning, build real-time journeys in Adobe Experience Platform, define XDM schemas and identity resolution, monitor dataflow health, lead Agile delivery pods, translate strategy to technical requirements, and create executive-level measurement and governance frameworks.

Lead proactive, hypothesis-driven threat hunts using SIEM/EDR/log data to identify adversary activity. Design and maintain hunt playbooks, analyze network and system logs, collaborate with SOC/IR/Red Team, present findings to leadership, and mentor other security staff to strengthen detection and response.

Lead definition and execution of global target-state fraud architecture and roadmap. Drive application rationalization, end-to-end fraud journey mapping, cloud and AI integration (AWS, Generative/Agentic AI), risk and controls governance, and stakeholder engagement to standardize, simplify, and scale fraud capabilities across regions and channels.

Serve as the technical Product Owner on an Agile Scrum team: maintain and prioritize backlog, translate business and customer needs into user stories, align product roadmaps with architecture, manage dependencies across stakeholders and vendors, and measure product performance to drive AI-, data-, and cloud-enabled product delivery.

Design, prototype, and deploy AI-powered solutions by partnering with business stakeholders and engineering teams. Analyze processes, identify AI automation opportunities, build POCs/MVPs using LLMs and agents, define KPIs, document and train users, and drive adoption and scalability of reference AI solutions across the organization.

Lead and scale AI, ML, predictive analytics, and intelligent automation for Revenue Intelligence. Identify high-value use cases, build and deploy LLM and agentic AI solutions and production models (forecasting, propensity, recommendation, customer intelligence), drive automation, measure adoption and business value, and partner cross-functionally while managing vendors and delivery frameworks.

Senior Business Systems Analyst responsible for capturing functional requirements for agent-facing servicing applications, defining integrations/APIs with core banking systems, translating needs into user stories and acceptance criteria, supporting testing and releases, analyzing workflows to improve agent productivity, and identifying AI/ML opportunities for fraud detection and automation.

Lead strategy, roadmap, and delivery of AI-powered and digital growth marketing capabilities. Partner across Product, Data Science, Engineering, Risk, and Marketing to design, test, and scale LLM and personalization solutions, define success metrics and governance, run experimentation, and drive measurable acquisition, engagement, and efficiency outcomes.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Lead design and implementation of enterprise and cloud security architectures (AWS/Azure/GCP). Develop security strategies, conduct cloud and IAM assessments, recommend tools/processes, and guide deployment and incident response improvements. Advise on frameworks (NIST, ISO, COBIT), zero-trust, SIEM, vulnerability management, and emerging tech risks. Train client teams and drive compliance and remediation.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

Lead design, development, and deployment of enterprise-scale AI/ML and agentic AI solutions. Architect end-to-end systems using Python and Java, fine-tune and optimize LLMs, implement RAG and prompt engineering, build CI/CD pipelines with containerization and orchestration, integrate SQL/NoSQL databases, enforce Responsible AI and compliance, and mentor teams while driving engineering best practices.

Design, develop, and deploy AI-driven applications using Generative AI and LLMs. Manage projects from conception to deployment and ensure compliance with regulations.

Design, develop, and deploy machine learning models and scalable ML pipelines using structured and unstructured data. Build production-ready software, APIs, and microservices; implement MLOps practices (CI/CD, versioning, monitoring); optimize models and conduct distributed training. Collaborate cross-functionally, ensure data privacy/security, document systems, and participate in Agile delivery.

Lead and oversee enterprise ML and statistical modeling efforts, build and mentor data science teams, ensure model governance and compliance, collaborate cross-functionally to deliver scalable AI solutions that drive business outcomes, and advance research and adoption of cutting-edge ML methods.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

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.