Machine Learning Jobs in Plano, TX

Lead enterprise architecture for large-scale software and platforms, evaluate and integrate AI and emerging technologies, develop architecture assessment tools, drive governance, produce secure production code, and provide technical leadership across teams to ensure scalability, resiliency, security, and operational excellence.

Lead engineering and ML platform work: design, develop, and maintain secure, scalable production code and ML infrastructure; drive adoption and validation of enterprise AI-assisted development practices; automate remediation, evaluate vendors, and coach teams on responsible AI, resiliency, and security within agile CI/CD workflows.

Design, build, deploy, scale, and maintain production machine learning systems and models. Lead technical design, model and application development, data pipeline construction, cloud architecture, testing, CI/CD, monitoring, governance, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams while leading teams developing enterprise AI coding tools.

Lead and scale engineering teams to transform the network into an Intent-Based, Infrastructure-as-Code platform. Drive automation, AI integration, microservices, cloud deployments, and engineering practice maturity. Mentor engineers, collaborate with product managers, improve resilience and scalability, and replace manual operations with intelligent network automation.

Lead design-driven technical solutions that blend UX and software development. Create service flows, wireframes, prototypes, and CX strategies; guide technical teams and integrate user research, AI, and accessibility into products.

Lead product management for enterprise product and experience initiatives, partnering with design, data, ML, and engineering to define strategy, prioritize roadmap, prototype solutions, and deliver customer-centric, technology-driven products that achieve business outcomes.

Lead enterprise product and experience initiatives, guiding cross-functional teams (product, data, design, ML, engineering) to define strategy, deliver customer-centric digital products, prototype and apply emerging AI/automation tools, and drive transformational outcomes across Capital One.

Leads development of AWS-native software engineering and automation solutions, including IaC, CI/CD, provisioning, observability, and lifecycle orchestration. Builds secure, resilient agentic AI integrations using LLMs and cloud services, improves operational stability, evaluates architectures and vendors, and promotes AI-assisted engineering practices. Mentors teams, establishes validation and responsible AI standards, and leads software engineering communities of practice.

Leads large-scale AI and machine learning initiatives, defining problem statements, roadmaps, and production solutions. Develops NLP, generative AI, recommendation, personalization, search, and document question-answering models. Builds scalable batch and real-time ML pipelines on AWS, collaborates with product, engineering, business, legal, and compliance teams, and communicates technical results to senior stakeholders. Oversees experimentation, model evaluation, deployment, and team performance.

Leads hands-on architecture and implementation of production-grade generative AI systems, including search, conversational AI, and agentic workflows. Designs retrieval pipelines, orchestration, dialogue management, and scalable backend systems while optimizing latency, reliability, observability, cost, and evaluation. Serves as the senior technical authority for LLM-powered applications, establishes engineering standards, conducts design and code reviews, mentors engineers, and partners with product and business stakeholders. The role requires deep software engineering, AWS, distributed systems, and regulated-environment experience.

Build and maintain production generative AI applications for banking, including search, conversational AI, retrieval pipelines, and agentic workflows. Write and review code daily, integrate LLM components, improve system latency and reliability, and implement observability and error recovery. Collaborate with technical leaders, product teams, and business stakeholders while contributing to architecture, design reviews, engineering standards, and testing practices in a regulated environment.

Provide legal and regulatory monitoring and analysis for Consumer & Community Banking; draft summaries; manage regulatory and change-management matters; design and improve legal processes; evaluate and apply emerging technologies and AI prompts for legal workflows; respond to regulator, audit, and compliance inquiries; support special research projects and collaborate across teams to solve problems and prioritize work in a fast-paced environment.

Leads enterprise AI platform strategy, architecture, governance, security, and multi-year roadmaps. Defines scalable capabilities for generative AI, agentic AI, machine learning, and advanced analytics across business domains. Partners with executives and technical teams to establish standards, risk controls, operating models, and adoption strategies. Oversees architecture decisions, platform design, assessments, incident support, mentoring, and responsible AI practices in a regulated environment.

Lead enterprise payments architecture for card-present solutions, provide technical governance and guidance, integrate AI-enabled capabilities securely, develop and review production code, evaluate emerging technologies, and drive architecture decisions across product families.

Lead architecture for global cloud-based eCommerce payment platforms. Advise stakeholders on design and technology selection, define multi-year roadmaps, apply AI-enabled architecture patterns, ensure security/compliance, evaluate vendors, and present strategies to senior leaders to modernize merchant acquiring, payment gateway, and developer-facing APIs.

Lead architecture for data, reporting, and analytics product family: set target-state designs, guide technology evaluation and AI-enabled integrations, produce and review production code, enforce governance, resiliency, security, and auditability, and provide technical leadership across teams and vendors.

Leads the Underwriting Data Engineering team and oversees the design, development, and productionization of scalable data and machine learning pipelines. Responsibilities include feature engineering, data preparation, model deployment, data validation, governance, monitoring, API integration, analytic data products, roadmap development, stakeholder collaboration, and team coaching.

Lead the architectural vision and transformation of business support systems into a scalable, cloud-native ecosystem. Design microservices, SaaS, event-driven, omnichannel, and domain-driven solutions across customer management, product catalog, ordering, billing, and revenue domains. Integrate AWS, Azure, GCP, APIs, and AI/ML capabilities, establish architecture governance, and ensure security, privacy, compliance, reliability, and observability. Mentor architects, collaborate with cross-functional teams, and influence senior leadership and executive decisions.

Lead and deliver advanced analytics and machine learning solutions to support pricing, revenue optimization, and partner relationships. Collaborate with cross-functional teams to design, validate, and implement models at scale using large transaction datasets and modern big-data tooling. Translate technical insights into actionable business recommendations.

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.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n while collaborating with AI, data, and platform teams.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n. Collaborate with AI, data, and platform teams on production-ready solutions while developing applied Generative AI engineering skills.

Develop, validate, maintain, and monitor machine learning models and data pipelines. Analyze forecasting, experimentation, and statistical problems, document model assumptions and limitations, and explain results to non-technical stakeholders. The role involves cloud data platforms, productionizing models, and contributing to model validation and monitoring standards while partnering with data science, analytics, and business teams.

Leads applied machine learning and data science strategy, roadmap, and delivery. Manages and develops data science talent while overseeing statistical, AI, and machine learning model development. Partners with senior business stakeholders to frame complex problems, deliver scalable solutions, communicate tradeoffs, and drive measurable outcomes. The role includes hands-on technical leadership, model robustness oversight, production deployment and monitoring, and cross-functional influence across enterprise functions.

Lead architecture design and governance for trade surveillance products, provide technical direction, integrate enterprise AI securely and auditable into architectures, develop and review production code, and establish reusable SDLC/toolchain patterns.