Top AI & Machine Learning Jobs

Lead a team that builds a frictionless enterprise AI intake, enablement, and governance experience. Design self-service standards, streamline routing and adoption, remove blockers, form cross-functional partnerships, mentor staff, and present executive-level findings to accelerate safe AI deployment across the company.

Lead the architecture, development, deployment, and maintenance of enterprise-scale machine learning and Generative AI systems. Design ML pipelines, RAG applications, autonomous AI agents, NLP solutions, knowledge graphs, and cloud-based inference infrastructure. Establish MLOps standards, CI/CD, monitoring, governance, and model optimization practices. Evaluate emerging AI technologies, advise executives and stakeholders, mentor data science and engineering teams, and deliver production LLM, conversational AI, and intelligent automation solutions.

Deploys and tunes agentic AI solutions embedded within business units. Responsibilities include building enterprise system integrations and lightweight data pipelines, normalizing data, developing RAG and prompt components, debugging production systems, running A/B experiments, validating ROI, documenting failure modes, and partnering with operational stakeholders. The role requires production Python, SQL, cloud deployment, LLM integration knowledge, and enterprise data integration experience.

Lead technical direction for agentic AI across Forward Deployment Engineering. Build shared foundations, set architecture and standards, stay hands-on writing and reviewing production code (Python), solve complex LLM/agent problems, ensure data security and compliance, and mentor engineers to improve delivery speed and ROI.

Own AI cost visibility and optimization: design showback/chargeback and cost-optimization models for GenAI/LLMs, manage enterprise license and seat programs, monitor/analyze spend, flag anomalies, feed dashboards and reporting, and partner with engineering and finance to reclaim unused spend and ensure accurate cost attribution.

Lead continuous testing and evidence generation for AI controls, map controls to SOX, NIST AI RMF, and ISO 42001, track audit remediation, detect control drift, and partner with Cyber, Legal, and AI Trust teams to maintain independent assurance.

Lead design and delivery of agentic LLM solutions embedded in business units: scope requirements, build RAG pipelines, integrations, orchestration, mentor FDE engineers, run experiments, measure ROI, and produce domain reporting and playbooks.

The Senior Engineer, Enterprise AI designs and develops AI applications that enhance productivity and decision-making across T-Mobile's enterprise, utilizing various AI frameworks and collaborating with cross-functional teams.

Lead design and strategy for AI-driven conversational experiences: build conversation architectures, response patterns, structured content models, and interaction frameworks. Partner with Product, UX, Engineering, and ML teams to ensure explainable, controllable AI behavior, optimize via research and analytics, run cross-functional workshops, and mentor other designers to scale conversation design across the enterprise.

Lead and scale a Forward Deployment Engineering practice that embeds AI/agentic engineering pods across business units. Drive technical quality, cross-pod coordination, resourcing, senior stakeholder relationships, practice health, responsible AI standards, and ensure pods deliver validated ROI while coaching managers and overseeing hiring and delivery operations.