Top AI & Machine Learning Jobs

Leads the strategy, architecture, development, and implementation of scalable data and AI products for Finance, Procurement, and Supply Chain. Oversees cloud-based platforms, data pipelines, integrations, lakehouse development, governance, security, and enterprise information solutions. Builds and mentors leadership teams, drives platform adoption, evaluates vendors and technologies, and partners with business and technology stakeholders to deliver measurable business outcomes.

Develop and maintain forecasting and machine-learning models for marketing performance, budget allocation, and customer acquisition trends. Diagnose model failures and drift using Python, R, and SQL; conduct scenario forecasting, statistical validation, experimentation, and media attribution analysis. Build dashboards in Tableau and Power BI, collaborate with marketing, finance, analytics, and data engineering teams, and scale models into production environments.

Leads technical innovation in data science by developing and deploying advanced analytics, machine learning, deep learning, and generative AI solutions. Responsibilities include preparing complex datasets, evaluating emerging technologies, standardizing modeling toolkits, collaborating on data architecture and governance, guiding technical teams, and communicating data-driven insights to business leaders.

Build and deploy agentic AI solutions embedded in business units. Develop integrations with CRM, billing, ticketing, ERP, and other enterprise data sources; create lightweight data pipelines; tune prompts and agent behavior; run production A/B experiments; validate ROI; debug systems; document performance and failure modes; and partner with business stakeholders. The role requires Python, SQL, cloud, Docker, LLM integration, data security, responsible AI, and production engineering experience.

Leads end-to-end delivery of agentic AI systems embedded within business units. Designs LLM applications, RAG pipelines, prompt orchestration, multi-agent workflows, integrations, and data pipelines for production environments. Owns architecture, experimentation, ROI measurement, data quality, stakeholder communication, and domain reporting. Mentors deployed engineers, establishes reusable engineering patterns, supports responsible AI and data security, and guides solutions from prototyping through production hardening.

Own the Responsible AI evaluation framework, evaluation program, and AI transparency standard. Define product vision, strategy, roadmap, features, user stories, and backlogs for AI evaluation tests and benchmarks. Partner with Legal, Privacy, AI engineering, and business stakeholders to prioritize evaluations, coordinate releases, monitor quality, and close findings. The role requires technical experience, product management expertise, Agile SDLC knowledge, and familiarity with Responsible AI frameworks and regulated AI applications.

Serves as the liaison between the business unit and AI Center of Excellence. Manages AI use-case intake, prioritization, governance, compliance, vendor and platform evaluations, proof-of-concept pilots, and financial stewardship. Translates business needs and technical AI capabilities, maintains model inventories, supports responsible AI and risk assessments, and drives adoption through enablement, training, change management, and AI literacy initiatives.

Manages T-Mobile’s AI enablement strategy for its shared services organization. Coordinates AI initiatives with IT and the AI Center of Excellence, governs tool adoption and data compliance, evaluates business value, designs agentic workflows, and drives productivity adoption of platforms such as Claude and Microsoft Copilot. The role also maintains AI data-readiness requirements, establishes operational guardrails, tracks portfolio outcomes, and translates enterprise AI policies into practical service-delivery standards.