Machine Learning Jobs in Irving, TX

Lead product strategy and delivery for AI-enabled equipment and vendor finance originations. Influence leadership, define vision and roadmap, prioritize backlog, engage cross-functional stakeholders, drive large-scale modernizations, ensure compliance and measurable outcomes, and embed data-driven decision making.

Lead data architecture organization to design and deliver AI-ready data foundations, domains, and data products for Physical AI, autonomy, and digital twins. Define scalable data patterns for collection, ingestion, processing, quality, governance, annotation, and availability to accelerate model development, deployment, and continuous learning across edge-to-cloud systems.

Join an Agile Scrum team as the Product Owner responsible for maximizing development output, maintaining and prioritizing the product backlog, crafting customer-centric user stories, aligning roadmaps across stakeholders, measuring product performance, partnering with vendors, and ensuring on-time delivery of key features.

As a Data Scientist in the Innovation Team, you'll develop machine learning algorithms on molecular sequencing data to enhance cancer diagnostics and treatment, collaborating with diverse experts on high-impact projects.

Lead architecture and development of scalable, secure systems; set engineering standards and CI/CD practices; mentor engineers; drive SRE/DevOps collaboration; adopt AI/ML tools; own incident response, root-cause analysis, performance optimization, IaC and cloud-native improvements; align technical roadmaps with product and business goals.

The Cybersecurity AI_ML Engineer develops and maintains security systems using AI and machine learning, implements threat detection, analyzes security metrics, and collaborates on security incidents.

Lead Solution Architect provides technical leadership for digital transformation, defines end-to-end Azure/cloud architectures, mentors architects, sets standards and patterns, drives integration and automation, and partners with business and technology leaders to deliver scalable, secure mobile and web solutions.

The role leads application systems analysis and programming, drives architecture strategy, evaluates technologies, and influences senior stakeholders in the investment domain.

Design, build, deploy, monitor, and optimize unattended Power Automate RPA workflows. Collaborate with business and IT partners, ensure compliance and security, document solutions, perform testing and code reviews, support production, and cross-train on Automation Anywhere.

Lead strategic design, implementation, and scaling of Palantir Foundry/Gotham data solutions. Oversee cross-functional teams, manage full project lifecycle, deliver data integration, governance, analytics, and ML capabilities, and engage stakeholders and clients to translate business needs into scalable Palantir-powered solutions.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Develop and execute data-driven marketing strategies that use AI, machine learning, and automation to drive brand awareness, customer acquisition, and revenue. Perform market research, competitive analysis, campaign optimization, CRM and marketing automation integrations, SEO/SEM and paid media optimization, dashboarding, and cross-functional collaboration with sales, IT, and product teams to measure and improve marketing ROI.

The Global Financial Crimes Analyst evaluates and implements technologies to enhance compliance and risk detection in financial crimes. Responsibilities include collaborating with functional teams, developing automation solutions, analyzing vendors, and supporting compliance initiatives.

Design, build, and maintain large-scale data structures, ETL pipelines, and data marts. Integrate and operationalize ML models, optimize Hadoop/HDFS workflows, ensure data quality, and collaborate with data science to support analytics and reporting needs.

Build, deploy, and monitor ML/AI models to automate processes and generate insights. Collaborate with stakeholders, Data Engineers, and Product Managers to design approaches, train and compare models, analyze data distributions, detect adversarial trends, and deliver production-ready solutions aligned to business KPIs.

Build, develop, and maintain cloud-native web applications and large-scale data pipelines. Design ETL processes, data architectures, and data marts; integrate ML models; collaborate on CI/CD, API integration, and production operations to support analytics and reporting.

Partner with business and technology teams to identify, define, and support AI, analytics, and automation solutions. Gather requirements, translate use cases, support POCs, testing, deployment, monitoring, governance, training, and continuous optimization to drive business value.

Technical Product Owner for a Digital Payments platform owning backlog, roadmaps, PSP integrations, and feature delivery (real-time payments, wallets, fraud prevention). Collaborates with engineering, vendors, and stakeholders to design, prioritize, and release secure, scalable payment solutions while driving continuous improvement and product performance measurement.

Lead design and implementation of enterprise and cloud security architectures (AWS/Azure/GCP). Develop security strategies, conduct cloud and IAM assessments, recommend tools/processes, and guide deployment and incident response improvements. Advise on frameworks (NIST, ISO, COBIT), zero-trust, SIEM, vulnerability management, and emerging tech risks. Train client teams and drive compliance and remediation.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, integrate and fine-tune various LLMs, and deploy containerized FastAPI services with monitoring, CI/CD, and performance/cost optimizations.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.