Machine Learning Jobs in Richardson, TX

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, constructing distributed data pipelines, operating cloud-based systems and Kubernetes environments, automating testing and deployment, monitoring models, and applying responsible AI practices. The role collaborates with product and data science teams in an Agile environment and may provide technical leadership for scalable ML solutions.

Lead design and production of generative and agentic AI solutions, build ML pipelines and MLOps, define enterprise semantic modeling and ontology governance, implement RAG and responsible AI practices, and mentor engineers while partnering with stakeholders to deliver measurable business outcomes.

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 software and ML engineering for scalable, secure production systems. Author and review code, drive AI-assisted engineering practices, design ML pipelines and deployment/monitoring, automate remediation, evaluate vendors, and mentor teams on responsible AI and MLOps.

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.

Lead design, development, and production deployment of advanced AI/GenAI and LLM solutions. Build and tune models, integrate generative AI into ML platforms, drive MLOps adoption, ensure responsible AI governance, collaborate cross-functionally, and mentor AI engineers.

The Associate Actuary will apply actuarial analytics, financial forecasting, valuation, and modeling to support corporate actuarial, finance, risk, and product functions. Responsibilities include developing dashboards and visualizations, analyzing financial and operational results, modernizing actuarial models and data platforms, and evaluating AI, machine learning, automation, and emerging technologies. The role also requires cross-functional collaboration, process improvement, governance, testing, and communicating complex technical findings to business and executive stakeholders.

Design, build, deploy, retrain, monitor, and maintain production machine learning models and infrastructure. Develop application code, data pipelines, testing, automation, and cloud-based architectures using distributed systems and containerized environments. Collaborate with Product and Data Science teams in Agile delivery, apply responsible and explainable AI practices, optimize model performance, and support resilient deployments at scale.

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.

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.