Machine Learning Jobs in Irving, TX

Design and deliver workforce analytics and predictive models using ML/AI. Acquire, clean, and explore structured and unstructured data, lead analytics projects, create visualizations, and communicate data-driven insights to business leaders to influence workforce decisions.

Lead hands-on designer for AI-enabled ITSM capabilities (Incident, Change, Problem, SACM, Business App, SLM). Translate workflows into scalable AI and automation solutions, prototype and validate designs, coach product teams, and partner with engineering, architecture, platform, and governance to deploy repeatable, risk-aware AI patterns.

Lead strategy, roadmap, and delivery of AI-powered and digital growth marketing capabilities. Partner across Product, Data Science, Engineering, Risk, and Marketing to design, test, and scale LLM and personalization solutions, define success metrics and governance, run experimentation, and drive measurable acquisition, engagement, and efficiency outcomes.

Lead technical program and application support for contact center systems (chat, chatbot, IVR). Partner with product, engineering, and business teams to define scope, document requirements, analyze system logic, identify risks, and implement solutions. Coach junior analysts, drive integration across technology areas, and support GenAI/ML-enabled automation to ensure high availability and performance.

Lead solution design by partnering with stakeholders to gather requirements, evaluate technologies, and develop business cases. Build and support applications using low-code, RPA, ML, GIS, and traditional development (.NET, Python, SQL). Drive data modeling and warehouse efforts, vendor selection, and transition projects to IT PM for delivery while providing ongoing support and continuous improvement.

Lead marketing analytics, predictive modeling, experimentation, and measurement. Build segmentation, personalization, attribution and MMM frameworks; partner with marketing, data engineering, and IT to operationalize models and deliver executive reporting and optimization.

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 design and implementation of scalable, domain-driven web architectures. Build high-performance backend services (Node.js/TypeScript), responsive React frontends (TanStack), and PWAs (Service Workers/Workbox). Design REST and GraphQL APIs, event-driven systems, data synchronization for intermittently connected clients, and manage client/server persistence (IndexedDB/Dexie, SQLite, PostgreSQL). Deploy cloud-native solutions on AWS (EKS, S3, SQS, SNS, API Gateway, Cognito). Mentor engineers and ensure engineering best practices, testing, and regulatory alignment.

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.

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.

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.

The Lead AI Engineer will manage AI/ML model production, ensure deployment pipelines, mentor teams, and enhance AI systems with innovative technologies.

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.

Lead design, development, and deployment of enterprise-scale AI/ML and agentic AI solutions. Architect end-to-end systems using Python and Java, fine-tune and optimize LLMs, implement RAG and prompt engineering, build CI/CD pipelines with containerization and orchestration, integrate SQL/NoSQL databases, enforce Responsible AI and compliance, and mentor teams while driving engineering best practices.

Design, build, and operate high-performance ML serving platforms for large models. Focus on request routing, batching, autoscaling, GPU utilization, observability, caching, deployment workflows, incident response, security controls, and collaboration with ML/product teams to reliably serve LLMs, vision, and recommendation workloads in production.

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 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.