Machine Learning Jobs in Richardson, TX

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.

Drive enterprise AI transformation across the Marketing organization by managing the AI roadmap, governance council, intake processes, use-case prioritization, training, change management, responsible AI practices, and adoption KPIs. The role coordinates cross-functional stakeholders, translates technical capabilities into business plans, develops enablement resources, and reports progress and ROI to senior leadership. This is an execution and organizational adoption role rather than a technical AI development position.

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.

Architect and scale enterprise software for experimentation, audience segmentation, and customer journey decisioning across marketing and messaging platforms. Build high-performance, AI-powered systems using machine learning, agentic workflows, distributed streaming, and personalization. Partner with data science, product, and engineering teams; mentor engineers; and drive technical excellence, experimentation, and innovation.

Owns end-to-end endpoint detection coverage, including telemetry requirements, behavioral detection engineering, alert deployment, tuning, and coverage gap analysis. The role develops Detection-as-Code rules, applies MITRE ATT&CK and threat research, leverages AI and machine learning, collaborates with security operations and threat intelligence teams, and mentors engineers. Responsibilities include endpoint log analysis, EDR engineering, adversary emulation alignment, risk communication, compliance documentation, and high-fidelity detection development at enterprise scale.

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.

Leads control management for JPMorganChase’s Consumer and Community Banking Operations portfolio. Designs and strengthens risk and control frameworks, identifies emerging compliance and operational risks, drives governance and issue management, and promotes consistent agile control practices. Partners with Product, Technology, Legal, Compliance, Audit, and Risk stakeholders to support transformation initiatives, regulatory compliance, control effectiveness, and operational resilience. Requires strong risk-based decision-making, change management, stakeholder influence, executive communication, and data literacy.

Leads the Independent Agency Data Science team and oversees Quote Efficiency models, emerging AI/ML initiatives, and products supporting agents, territory managers, and producers. Manages 7–10 data scientists across model development, deployment, monitoring, and performance evaluation. Partners with analytics, technology, strategy, marketing, and business stakeholders while developing talent and communicating with senior executives.

Design and implement data science, machine learning, generative AI, and process automation solutions for regulatory operations. Develop analytical models, LLM applications, dashboards, ETL workflows, and automated processes using Python, SQL, SAS, Alteryx, Tableau, AWS, and Snowflake. Establish data quality, model governance, KPI, and technical standards; translate findings into executive insights; and collaborate with stakeholders to improve efficiency, compliance, and financial outcomes.

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.

Own the strategy, roadmap, and delivery of Applicant Security products addressing identity fraud, resume fraud, bot activity, and abuse. Partner with engineering, data science, cybersecurity, legal, HR, and vendors to define requirements, prioritize backlogs, manage releases, establish success metrics, and ensure privacy and compliance. Lead third-party security vendor evaluation and governance while balancing risk reduction with candidate experience in a complex enterprise environment.

Leads control strategy and risk management for Consumer and Community Banking. Responsibilities include designing control frameworks, conducting continuous risk assessments, detecting and remediating control issues, performing root cause analysis, using analytics and automation, partnering with Legal, Compliance, Audit, and Risk, and developing a high-performing team. The role focuses on strengthening compliance and operational risk controls across a large, matrixed banking organization.

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.

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.

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 the design, development, deployment, and maintenance of production machine learning systems at scale. Responsibilities include building and validating models, optimizing ML infrastructure and data pipelines, automating testing and deployment, monitoring and retraining production models, applying cloud technologies, and ensuring responsible, explainable, secure, and highly available ML applications. Collaborates with Product, Data Science, and Agile engineering teams.

Design, build, deploy, and maintain machine learning models and applications at scale. Develop production-grade code, data pipelines, cloud-based ML architectures, and automated testing and deployment workflows. Monitor and retrain production models, optimize performance, and collaborate with Product, Data Science, and Agile engineering teams. Apply responsible AI, model governance, explainability, and security best practices.