Top AI & Machine Learning Jobs

Lead end-to-end data science engagements combining statistical modeling, machine learning, large-scale data transformation, and generative AI. Build agentic LLM applications, present analytical solutions to C-level stakeholders, lead workstreams, mentor junior data scientists, and collaborate with delivery and account teams. Develop reusable documentation and code libraries while contributing to internal research and education.

Develop and deploy AI/ML solutions for government customers, including data pipelines, model training, MLOps, deep learning, NLP, and computer vision systems. Collaborate with data scientists, software engineers, and government stakeholders; report model performance; write technical papers; mentor teammates; and communicate technical concepts through briefings and presentations.

Leads Trepp’s Data Science & Modeling team, overseeing hiring, mentoring, technical roadmaps, model design, validation, deployment, and production AI systems. Shapes enterprise AI strategy, identifies high-value use cases, advises executives on AI risks and capabilities, and partners with Product, Sales, and Engineering. The role requires hands-on expertise in data science, machine learning, LLMs, MLOps, cloud-native deployment, and financial data applications.

Leads enterprise AI and machine learning strategy, governance, product lifecycle, and roadmap execution. Builds and manages AI teams, oversees budgets and vendors, evaluates build-versus-buy decisions, and ensures production systems meet data privacy, model risk, tribal gaming, and responsible gaming requirements. Partners with senior IT and business leaders to define success metrics, secure funding, manage risk, train users, and deliver scalable AI products aligned with organizational goals.

Build and maintain scalable data pipelines and datasets for ML/AI workflows; collect, clean, and preprocess structured and unstructured data; ensure data quality, availability, governance, and security; collaborate with data scientists and ML engineers; optimize storage and troubleshoot pipelines.

Leads applied AI engineers and data scientists while owning AI solutions from business problem framing through production deployment, monitoring, retraining, and impact measurement. Establishes ML and MLOps standards, responsible AI governance, technical roadmaps, and build-versus-buy strategies. Partners with business, data engineering, and BI teams across restaurant operations, ordering, supply chain, and guest experience. Guides generative AI, forecasting, computer vision, and intelligent automation initiatives and communicates outcomes to senior leadership.

Build, configure, test, and refine no-code generative AI agents and automated workflows for business use cases. Translate stakeholder needs into agent instructions, workflow logic, requirements, and success criteria. Gather feedback, validate outputs, improve solution performance, and help business teams adopt AI responsibly. The role requires strong communication, technical aptitude, process improvement skills, attention to detail, and comfort working in a regulated environment.

Lead AI and machine learning engineering teams delivering production-grade classical ML, LLM, and agentic systems. Own solution architecture, context and harness engineering, fine-tuning pipelines, cloud platform strategy, and technical standards. Partner with clients and senior leadership while supporting pre-sales through proposals, demos, proofs of concept, and solution scoping. Manage and mentor engineers, oversee model development, and guide scalable, reliable, cost-efficient AI deployments.

Develops end-to-end machine learning solutions for pricing, forecasting, optimization, customer journey efficiency, and business growth. Responsibilities include data collaboration, A/B test design, classical and deep learning, time series and uplift modeling, error analysis, production preparation, model monitoring, and stakeholder communication.

Design, develop, and deploy advanced AI/ML models; optimize pipelines for performance and scalability; integrate solutions with cross-functional teams; research new techniques; mentor junior engineers; monitor and maintain production AI systems.