Machine Learning Jobs in Richardson, TX

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.

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.

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 technology risk and controls management within Identity and Access Management, including risk assessment, control governance, regulatory compliance, issue remediation, and executive reporting. Develops and matures Continuous Control Monitoring capabilities using automation, data analytics, AI, and machine learning to automate testing, detect exceptions, identify emerging risks, and improve control effectiveness. Partners with technology, data, product, business control, and regulatory stakeholders to strengthen the control environment.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Leads the design and development of scalable software frameworks and secure production code. Architects enterprise AI-enabled engineering workflows, establishes responsible AI guardrails, and improves automation, testing, release readiness, and incident response. Advises cross-functional teams, influences senior stakeholders, defines target-state AI architectures, and guides roadmaps and solution architectures across Consumer and Community Banking.

Lead data science and NLP initiatives for AI-powered products, partnering with engineers, machine learning specialists, and product managers. Build, fine-tune, evaluate, validate, and operationalize large language and machine learning models at scale. Apply advanced NLP, deep learning, and model training techniques while supporting model risk compliance. Translate technical findings into business outcomes and develop team talent.

Lead day-to-day program management and delivery of Client Onboarding & Service transformation initiatives. Scope problems, design solutions, plan delivery, produce executive-ready presentations, drive cross-functional alignment, manage change, engage product and tech teams using agile practices, and apply emerging tech insights to improve outcomes.

Build AI-driven governance and resilience solutions for enterprise cloud infrastructure. Design intelligent guardrails, distributed systems, microservices, and full-stack platforms using Go, Python, Java, TypeScript, AWS, Docker, and Kubernetes. Lead architectural vision, technical roadmaps, testing, deployment, and optimization while writing code, conducting reviews, mentoring engineers, and collaborating with product, data science, and risk teams.

Leads cybersecurity engineering teams developing enterprise security solutions across multiple technology domains. Clarifies security requirements, supports multi-level network security, performs threat modeling and vulnerability assessments, and advises leaders during security risks. Uses and validates enterprise-authorized AI for risk analysis, documentation, security testing, and control validation while ensuring data sensitivity, auditability, and resiliency. Coaches, develops, hires, and recognizes engineering talent.

Manage compliance and operational risk controls within Consumer and Community Banking. Partner with stakeholders to identify risks, assess control gaps, design and monitor controls, lead risk and control self-assessments, and support regulatory compliance initiatives. Use automation, AI, analytics, and visualization tools to improve control effectiveness and reporting. Lead projects, communicate findings, advise executives, and drive technology-enabled process improvements.

Leads a team overseeing operational and compliance controls for a banking operations product organization. Provides strategic guidance on responsible AI adoption, identifies and manages operational and technology risks, strengthens governance and control practices, resolves control gaps, and communicates risk insights to senior management. The role also leads strategic initiatives, partners with stakeholders, develops talent, and drives improvements to the control environment.

Advises Capital One’s Financial Services Division on auto finance, technology, AI, data privacy, intellectual property, antitrust, and consumer-finance regulations. Partners with executives, product managers, analysts, designers, and business teams to identify risks, support product development, guide marketing and sales strategies, and enable responsible innovation. Requires translating complex legal and regulatory issues into practical business solutions while influencing stakeholders and navigating ambiguity.

Leads software engineering and AI/ML delivery for large-scale risk technology systems. Designs complex architectures, develops secure production code, optimizes Spark clusters and data pipelines, and applies Python, PySpark, SQL, AWS, and Databricks. Establishes AI-assisted engineering governance, improves software quality and automation, analyzes large datasets, and leads technologists in delivering resilient, scalable applications.

Leads architecture and engineering for consolidating global wide area networks into a unified platform. Defines carrier-scale routing, traffic engineering, resiliency, automation, telemetry, validation, and AI-assisted operational strategies. Oversees transformation plans, migrations, production rollouts, technical standards, risk controls, and reliability improvements while partnering across engineering, infrastructure, cloud, security, operations, and architecture teams.

Leads a strategic insights team analyzing product development productivity, operating models, workflows, and AI adoption. Frames hypotheses, conducts quantitative analysis and experiments, evaluates delivery and business outcomes, and translates findings into executive recommendations. Partners with Product Operations, Technology, Product, and Finance to implement actions, track adoption, and measure results. Establishes analytical standards, communicates with senior leaders, and develops a high-performing analytics team.

Leads software engineering for large-scale big data and cloud-native platforms within risk technology. Responsibilities include designing and deploying AWS solutions, migrating legacy applications, developing secure production code, supporting operational stability, and guiding engineers, vendors, and technical teams. The role drives AI-assisted engineering governance, automation, testing, monitoring, and reuse across the software development lifecycle. It also requires expertise in Python or Java, machine learning, data pipelines, Spark, Databricks, and cloud technologies.

Lead cybersecurity strategy and architecture across products, applications, and technologies. Advise engineering teams, communicate complex risks to leadership, develop reusable security frameworks, create mitigation solutions, and evaluate emerging technologies. The role requires influencing cross-functional teams, shaping enterprise-wide architecture, and applying cloud-native, application, API, web, and mobile security expertise across a complex financial-services environment.

Leads software engineering for a Machine Learning I/O team, developing secure production code and guiding design, testing, debugging, and operational stability. Drives enterprise adoption and governance of AI-assisted development tools, establishes validation standards for AI outputs, promotes secure and responsible AI practices, and influences technical decisions across teams. Provides technical direction, serves as a subject matter expert, and advocates for scalable cloud-native engineering practices.

Leads development of full-stack, cloud-native software using Java, Python, AWS, microservices, APIs, and PostgreSQL. Designs scalable and secure systems, builds LLM and generative AI capabilities, and integrates AI agents into engineering workflows. Improves operational stability through automation and observability while contributing to Agile delivery, system architecture, and technical standards. Collaborates across initiatives and promotes responsible, secure validation of AI-assisted development outputs.

Build and fine-tune NLP and large language models to detect and categorize complaints and sentiment across client interactions. Develop taxonomies, multi-label classifiers, training and monitoring pipelines, and production deployments. Analyze model drift and bias, implement retraining strategies, and ensure compliance with privacy, regulatory, and model-risk standards. Collaborate with data engineering and MLOps teams and communicate model performance to technical and non-technical stakeholders.

Conduct model risk audits using econometric, statistical, and machine learning methods. Review enterprise models and risk practices, identify issues, develop analytical solutions, communicate findings to varied audiences, and maintain transparent documentation. Partner cross-functionally with business lines and build cloud-based, data-driven solutions. Required expertise includes regression, survival analysis, panel data, time-series analysis, machine learning, large datasets, and programming in R, Python, or SQL.

Design, deploy, and scale production AI/ML solutions for healthcare, including predictive models, generative AI, statistical analysis, microservices, APIs, and MLOps pipelines. Analyze complex clinical datasets, implement model monitoring and governance, evaluate emerging AI technologies, and collaborate with engineering, data, product, and clinical teams to improve care delivery and operational efficiency.

Designs, administers, and evolves Fisher Investments’ Azure data warehouse and analytics platform. Responsibilities include developing cloud data solutions, setting architecture and security standards, guiding developers, troubleshooting systems, monitoring operations, planning capacity, optimizing cloud costs, and supporting BI and data delivery teams. The role also coordinates cross-functional projects, provides after-hours support, and prepares technical work plans and status reports.

Leads teams developing semiconductor test automation platforms, test-program generation, ATE integrations, manufacturing data systems, and AI-driven applications. Establishes technical architecture and multiyear strategy across production lines and sites. Oversees reusable test-method libraries, analytics tools, dashboards, yield and quality monitoring, automated decisioning, and cost-of-test improvements. Partners with design, DfT, and process engineering teams to improve test methods, product quality, manufacturing scale, and cost.