Machine Learning Jobs in Irving, TX

Design, develop, test, and deliver scalable software applications, APIs, services, and user experiences. Build secure REST APIs, distributed services, event-driven integrations, and AI-enabled systems. Collaborate with product, architecture, data, quality, and business stakeholders while contributing to testing, CI/CD, observability, security, documentation, and operational readiness. Participate in design reviews and mentor engineers.

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.

Build and operate cloud-based MLOps platforms for analytical and generative AI models. Develop automated pipelines for model training, deployment, monitoring, governance, and lifecycle management. Partner with data science, engineering, security, compliance, and business teams to establish standards and deliver scalable, secure, production-ready AI solutions. Research and prototype emerging MLOps capabilities while connecting technical delivery to business and financial outcomes.

Leads enterprise data architecture strategy and governance for Citi’s fraud domain. Responsibilities include developing data models, flows, standards, and remediation frameworks; architecting scalable data solutions; supporting analytics and AI use cases; partnering with senior stakeholders; and managing data architecture analysts. The role requires extensive financial services experience, fraud-domain expertise, enterprise data architecture capabilities, executive communication, and cross-functional leadership.

Leads a team developing scalable Java software for counterparty credit risk management. Responsibilities include hands-on architecture and development, technical mentoring, code reviews, cloud-native and AI/ML adoption, project delivery, agile practices, stakeholder communication, and oversight of systems integrating trades, collateral, and market data.

Participate in Orion180’s 2027 summer internship program across accounting and finance, claims, customer experience, data science, sales, product, or specialty risk. Interns support meaningful projects, analysis, reporting, customer service, underwriting, product research, or business operations while receiving mentorship and professional development. The role is onsite in Melbourne, Florida; Irving, Texas; or Draper, Utah, with potential consideration for future co-ops and full-time positions.

Leads the design, development, deployment, and optimization of agentic AI, LLM, machine learning, and propensity modeling solutions. Builds models for customer behavior, churn, and lifecycle triggers; manages end-to-end MLOps pipelines; consolidates models; monitors performance; and provides technical mentorship. Presents model insights and strategic recommendations to senior leaders and business partners while driving hyper-personalized customer experiences.

Design and implement enterprise AI architectures spanning GenAI, machine learning, RAG, agentic workflows, data pipelines, APIs, and production deployments. Build Python prototypes and AI services, define governance and evaluation frameworks, integrate models with enterprise platforms, and guide engineering teams through implementation. The role requires hands-on expertise with LLM applications, orchestration frameworks, model lifecycle management, monitoring, security, and scalable production AI systems, preferably in financial services.

Leads enterprise solutions architecture for complex technology programs, translating business needs into scalable, secure systems. Designs distributed, cloud-native, event-driven architectures and integrates AI, ML, generative AI, and Pega workflow platforms. Develops technology roadmaps, governs architecture, evaluates risk, mentors technical teams, and guides modernization and investment decisions. The role partners with executives, engineering leaders, product teams, and global technology stakeholders to deliver resilient platforms supporting consumer banking operations.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Develop advanced data science and AI/ML solutions using complex healthcare data. Apply statistical modeling, machine learning, NLP, and LLM techniques, including RAG and prompt optimization. Build production-ready workflows integrating models with APIs, data pipelines, enterprise applications, and cloud platforms. Analyze data quality and model performance, communicate findings to technical and executive audiences, and collaborate across product, engineering, clinical, technology, and business teams.

Designs, develops, deploys, and operationalizes machine learning solutions using Python, PySpark, Azure technologies, and large-scale telemetry datasets. Builds scalable data pipelines, trains and evaluates models, applies MLOps practices, and communicates insights through dashboards and presentations. Collaborates with data engineers, data scientists, architects, product owners, and business stakeholders to deliver enterprise machine learning solutions.

Leads marketing measurement, attribution, predictive modeling, audience segmentation, and advanced analytics. Develops statistical and machine learning models, evaluates marketing and sales data, improves campaign and revenue measurement, and translates insights into strategic recommendations. Partners with marketing, sales, operations, digital, and data engineering teams while presenting findings to senior stakeholders and driving analytics innovation.

Leads principal engineers and cross-functional teams delivering business systems, products, and platform enhancements for Verizon’s indirect consumer channel. Responsibilities include managing technology roadmaps, requirements, business cases, vendor relationships, integrations, Agile delivery, stakeholder communications, and CRM, product catalog, and point-of-activation platforms. The role also drives AI-enabled store solutions, KPI performance, customer and partner experience improvements, and organizational change.

Leads digitalization and innovation initiatives for a remote optimization center, deploying AI, advanced analytics, automation, digital twins, and decision-support tools to improve plant performance. Oversees data science resources and analytics portfolios, partners with operations, engineering, technology teams, and vendors, and manages innovation programs and solution pipelines. The role drives solutions from ideation through production, adoption, scaling, and measurable operational improvement across manufacturing environments.

Designs, builds, and operates identity-resolution and master data management services for patient and provider records. Develops deterministic and probabilistic matching, merge and separation workflows, APIs, data models, audit trails, quality controls, and integrations across clinical systems. Maintains automated tests, observability, documentation, and controlled releases; supports incidents and root-cause analysis. Partners across product, governance, clinical operations, compliance, and engineering while mentoring engineers and using AI-assisted development workflows.

Lead global marketing for Caris AI Insights, defining positioning, messaging, GTM, and commercialization. Drive product launches, sales enablement, thought leadership, customer evidence, competitive analysis, and cross-functional alignment to accelerate adoption of AI-driven oncology intelligence across healthcare, pharma, and research customers.

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.

Develop automated analytics products and advanced data science solutions using machine learning, NLP, statistical modeling, predictive analytics, and generative AI. Lead end-to-end workflows from data preparation and experimentation through model validation, deployment, and visualization. Partner with product, engineering, and platform teams to move solutions from research into production, communicate findings to technical and non-technical audiences, and guide other data scientists.

Develop and deploy scalable data science products using machine learning, generative AI, NLP, statistical modeling, and predictive analytics. Analyze complex datasets, manage end-to-end modeling workflows, communicate insights through dashboards and presentations, and partner with engineering teams on production deployment. Provide technical guidance, apply responsible AI and model governance practices, and evaluate emerging technologies for business and clinical outcomes.

Leads the design, development, and deployment of advanced data science, machine learning, generative AI, and agentic AI solutions. Builds scalable analytics products, RAG systems, and evaluation frameworks; establishes technical standards and responsible AI governance; mentors data scientists; and partners with engineering teams to deploy and monitor production solutions. Translates complex analyses into actionable clinical, operational, and economic insights while guiding technical strategy and client-focused initiatives.

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.

Serve as a bridge between business and technical teams to identify, design, validate, and support AI, automation, and analytics solutions. Gather and document requirements, translate business needs into AI use cases, support POCs and deployments, monitor model performance and KPIs, ensure responsible AI practices, and drive adoption through training, change management, and continuous optimization.

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.

Lead test strategy, design test cases, and build automated test scripts and frameworks for enterprise applications. Execute manual and automated testing (functional, integration, performance, security), data/ETL validation, and defect analysis. Implement AI-driven and agentic automation solutions, mentor team members, collaborate with cross-functional teams, and continuously improve QA processes and tooling.