Machine Learning Jobs in Plano, TX

Develop and maintain Python-based automation tools, scripts, APIs, integrations, and workflows for telecom software deployments and network upgrades. Support AI-assisted operations, analytics, dashboards, troubleshooting, root-cause analysis, and automated recovery. Participate in Agile ceremonies, document technical solutions, gather requirements, and collaborate with engineering, delivery, support, project management, and customer-facing teams.

Leads compliance and operational risk management for Consumer and Community Banking. Partners with stakeholders to identify risks, design and monitor controls, conduct risk and control self-assessments, analyze KRI/KPI data, and deliver reporting. Supports regulatory compliance, control program projects, automation, AI, and continuous improvement while advising executives and driving risk mitigation.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Supports telecommunications equipment, network, and service issue resolution while assisting with repair, incident, and service-support processes. Analyzes operational data, identifies recurring issues, develops automation using scripting and AI, tests tools and workflows, maintains technical documentation and dashboards, and contributes to continuous improvement. Collaborates with engineering, operations, supply, quality, and customer-facing teams while preparing stakeholder recommendations.

Designs, deploys, maintains, and troubleshoots HPE NonStop payment infrastructure, secure key managers, Etinet servers, and related subsystems. Uses AIOps and AI-assisted practices for incident triage, anomaly detection, automation, documentation, and reliability improvements. Partners with SRE, observability, and cybersecurity teams to govern AI-enabled monitoring, maintain auditability, execute upgrades, and produce operational knowledge such as runbooks and troubleshooting guides.

Leads full-stack software engineering and infrastructure platform development for AI-driven, self-healing data center operations. Designs Java, Python, and Node.js applications, scalable APIs, CI/CD automation, cloud-native platforms, VMware integrations, and infrastructure-as-code workflows. Develops machine learning models for forecasting, anomaly detection, and test classification, integrating them into operational systems for predictive alerts and workload orchestration. Provides technical leadership, mentors engineers, reviews code, and drives firm-wide architecture and technology decisions.

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.

Leads data governance and ownership initiatives, defining data requirements, quality standards, metadata, lineage, controls, and curated datasets. Performs SQL-based testing, validation, reconciliation, and root-cause analysis. Partners with technology and business teams to mitigate data risk, support analytics and machine learning use cases, and improve data products. Applies AI and LLM tools to automate governance activities and contributes to roadmap, agile delivery, and stakeholder management.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

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.

Own the vision, strategy, roadmap, backlog, metrics, and end-to-end delivery of enterprise contact center capabilities. Integrate Amazon Connect workflows with business applications, lead discovery and stakeholder alignment, manage product readiness and adoption, and ensure secure, reliable, measurable outcomes across engineering, operations, risk, and business teams.

Leads cybersecurity architecture for software applications and platforms within wealth management. Drives secure product design, evaluates current and emerging technologies, establishes target architectures, and guides business and technical stakeholders. Oversees alignment with enterprise frameworks, regulatory requirements, scalability, resilience, and software development lifecycle practices. Serves as a subject matter expert in architecture governance and collaborates across teams to deliver secure, high-volume enterprise solutions.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Lead an agile engineering team to design, develop, test, and operate secure, scalable production software. Provide technical guidance, influence product and architecture decisions, deliver high-quality code, and promote firmwide engineering practices and modern cloud-native technologies.

Design and deliver secure software architecture, perform threat modeling and security risk analysis, implement and validate AI-assisted security workflows, produce architecture artifacts, identify gaps and mitigations, and collaborate with engineering teams to improve code quality and system resilience.

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

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.

Design and implement enterprise cybersecurity architecture for cloud-native applications. Embed security across SDLC and CI/CD, build Python automation for monitoring and remediation, evaluate vendors, lead practice communities, and collaborate with risk and compliance to translate regulatory requirements into technical controls while validating AI-assisted security outputs.

Nine-week summer analyst program within Operations supporting processing, trade/asset support, reporting, transformation, and data management. Responsibilities include transaction processing, reconciliations, client lifecycle support, data analytics and reporting, risk reporting, process improvement, and creating Tableau dashboards. Program includes training, networking, and may lead to full-time roles.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Lead oversight of AI/ML and cybersecurity operational risk across the firm: evaluate technology and cyber practices, identify threats, challenge first-line mitigations, conduct thematic analyses, assess AI/ML use cases, and collaborate with stakeholders to strengthen controls and ensure responsible AI adoption.

Perform cyber threat intelligence and research, build automations and AI-assisted workflows, support incident response and remediation, assess supplier cyber posture, design AWS infrastructure, integrate data sources, and communicate risk and mitigation recommendations to stakeholders.

Lead cybersecurity architecture for cloud-native applications within consumer banking: define target state, assess risks, propose mitigations, drive automation, lead vendor evaluations and communities of practice, and communicate solutions to senior stakeholders.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.