Top Tech Jobs & Startup Jobs

Lead the technical vision, architecture, roadmap, resourcing, and delivery for an enterprise digital product domain. Guide engineering teams building secure, scalable applications, APIs, data pipelines, and AI/ML capabilities. Coordinate Analytics, Data and ML, Application Engineering, MLOps, and DevOps teams; resolve dependencies; ensure reliability, testing, observability, security, and performance; and communicate technical trade-offs to senior stakeholders. Remain hands-on with production code while coaching engineers and supporting global platform deployment.

Leads account management and commercial growth for regional and national foodservice customers across QSR, full-service, and fast-casual channels. Develops annual business plans, forecasts sales and profitability, conducts business reviews, negotiates programs, identifies competitive opportunities, and engages executive buyers. Partners with bottlers, marketing, finance, revenue growth management, and operations to execute beverage strategies, resolve service issues, improve outlet-level execution, and expand customer partnerships.

Design, develop, test, deploy, and support scalable full-stack applications for sales execution, promotions, deals, and customer workflows. Build React-based web experiences, Salesforce solutions, backend services, REST APIs, database integrations, and enterprise data flows. Troubleshoot across application layers, contribute to architecture and code reviews, maintain CI/CD practices, improve legacy systems, and own solutions through production monitoring and support.

Provides executive and team administrative support through calendar management, travel and expense coordination, meeting logistics, document preparation, records maintenance, and stakeholder communication. Coordinates office relocation activities with Facilities, Procurement, and IT, manages vendors and workspace readiness, supports onboarding and operational processes, and contributes to project tracking and process improvement. The role is based onsite in Plano, Texas, Monday through Friday.

Design and develop ML and AI models using large structured and unstructured data, collaborate with stakeholders to translate business requirements, operationalize models with MLOps/CI-CD, produce visualizations and reports, maintain documentation and reusable data science assets, and communicate results to technical and non-technical audiences.

Leads machine learning and advanced analytics initiatives across global business functions. Develops and operationalizes end-to-end AI/ML solutions, manages data science teams, mentors talent, oversees vendors, and communicates analytical results to executives and technical stakeholders. Responsibilities include modeling, data wrangling, MLOps collaboration, cloud analytics, deployment automation, documentation, reusable solution development, and continuous monitoring.

Build and evolve Azure-based data platforms and customer segmentation capabilities. Develop production machine learning models, feature engineering workflows, training and inference pipelines, APIs, and MLOps processes. Ensure data quality, model monitoring, governance, privacy, security, and scalability. Partner with product, engineering, business, security, privacy, and legal stakeholders to deliver reusable data and ML solutions that improve customer engagement and commercial outcomes.

Design, develop, test, deploy, monitor, and support full-stack applications for sales execution workflows. Build React-based web experiences, Salesforce solutions, backend services, REST APIs, database integrations, and enterprise data flows. Troubleshoot cross-platform issues, implement automated testing and CI/CD, participate in architecture and code reviews, modernize legacy systems, and collaborate with product, architecture, data, QA, and security teams.

Design, develop, test, deploy, and support full-stack applications for sales execution, promotions, deals, and customer workflows. Build React-based web experiences, Salesforce solutions using LWC and Apex, backend services, REST APIs, database integrations, and enterprise system integrations. Own solutions across the lifecycle, troubleshoot complex cross-layer issues, improve application performance, participate in technical reviews, and support automated testing, CI/CD, monitoring, and production operations.

Leads technical direction for end-to-end machine learning and data systems powering digital products. Builds feature pipelines, predictive and recommendation models, training and inference workflows, APIs, and MLOps capabilities. Owns production deployment, monitoring, retraining, reliability, governance, and responsible AI practices while partnering with Product, Design, Data Science, and Engineering. Mentors engineers and establishes reusable standards for scalable ML and data platforms.