Machine Learning Jobs in Plano, TX

Leads system architecture and engineering across software, cloud, platform, and network components. Translates business requirements into scalable technical solutions, defines interfaces and non-functional requirements, guides development teams, and supports Agile delivery. Develops web applications using Node.js, Laravel, PostgreSQL, PowerShell, and Azure services. Performs automation, data analysis, machine learning, compliance, reliability, documentation, and accessibility testing using WCAG, WAI-ARIA, and assistive technology tools.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production services, and applying responsible AI and governance practices. The role collaborates with product, data science, and Agile engineering teams to solve complex business problems.

Design, build, deploy, and operate production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, and distributed ML systems; automate testing and deployment; monitor and retrain models; and apply responsible AI, governance, and explainability practices. Collaborate with Product, Data Science, and Agile engineering teams to solve complex business problems at scale.

Build, deploy, and maintain production machine learning models and infrastructure. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software engineering practices. The role partners with Product and Data Science teams in an Agile environment and uses technologies including Python, PyTorch, TensorFlow, Spark, Kubernetes, and major cloud platforms.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

Apply statistical, machine learning, and causal inference methodologies to analyze large datasets and solve complex business problems. Develop predictive models, identify trends and correlations, generate actionable recommendations, and build prototype solutions. Collaborate on innovative ideas and communicate analytical findings through presentations and technical documentation for senior management and regulatory review.

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.

Supports financial crime compliance through AML transaction-monitoring implementation, model tuning, data analysis, validation, investigations, and control testing. Develops AML analytics using statistical measures and machine-learning algorithms, helps integrate AI into surveillance models, documents requirements and project artifacts, and prepares testing reports and corrective action plans. Collaborates with technology, operations, and business teams while managing data mapping, regulatory documentation, and workflow execution.

Lead development of cloud-based MLOps and ML platform components supporting model training, deployment, monitoring, and serving. Build AWS infrastructure and microservices, automate testing and release workflows, monitor platform health, optimize high-volume transactional systems, and apply secure AI-assisted development practices. Collaborate within an agile team to deliver scalable, stable, and secure technology solutions.

Develop AI/ML proofs of concept and deployable models for compliance and operational risk analytics. Responsibilities include analyzing structured and unstructured data, designing methodologies, building scalable models and data pipelines, applying machine learning, graph learning, LLMs, and agentic solutions, and documenting quantitative models for governance and validation. The role collaborates with data scientists, technology teams, risk professionals, and model validation groups in a regulated financial-services environment.

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.

Leads systems engineering activities across requirements engineering, systems integration, verification and validation, MBSE, architecture development, technical planning, risk management, and lifecycle management. Guides system and trade studies, manages critical project components, mentors engineers, supports technical teams and suppliers, and leads high-profile test events. The role supports aerospace and defense ISR and aviation systems and requires a Secret U.S. security clearance.

Supports AI product discovery, business requirements, analytics, dashboards, user research, and Generative AI experimentation. The intern collaborates with product, data science, technology, and business teams to identify opportunities, define MVPs, test AI-powered tools, communicate insights, and support project implementation and continuous improvement.

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.

Owns the roadmap, strategy, risk management, and continuous improvement of data platforms supporting financial-crime analytics, detection models, investigations, and AML operations. Responsibilities include requirements gathering, backlog prioritization, agile delivery, data analysis, model lifecycle management, monitoring, issue remediation, governance, and stakeholder communication across business and technology teams.

Leads systems engineering for next-generation airborne command and control aircraft. Responsibilities include requirements engineering, systems integration, verification and validation, MBSE, architecture development, technical planning, risk management, lifecycle management, and decision analysis. The role influences project direction, mentors senior engineers, manages critical components, and supports aerospace systems certification and testing. It requires extensive leadership, multidisciplinary development experience, and active Secret clearance.

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.

Leads global product process excellence and business transformation initiatives, converting drawing-based product knowledge into structured, governed digital definitions. Oversees product configuration, engineering platforms, PLM and ERP integrations, digital thread strategy, process standards, AI adoption, and organizational change. Partners across product development, manufacturing, IT, business transformation, and regional teams to improve data quality, traceability, configuration accuracy, order handoff, and manufacturing readiness.

Drive fraud strategy, policy, and analytics for money-movement fraud. Build and manage fraud rules, analyze moderately complex data sets using SQL/Python/SAS and BI tools, create predictive models and visualizations, consult stakeholders, and ensure fraud risk is identified and controlled across payment channels.