Top AI & Machine Learning Jobs

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Measures customer engagement impact on pipeline, renewals, account health, and predictive model performance. Conducts baseline, holdout, before-and-after, and control-group analyses; validates customer data and helps tune models. Builds reusable value analyses, dashboards, and reporting for leadership, Customer Success, and Data Science. Requires strong SQL, Python, statistics, experiment design, business communication, and experience translating messy data into trusted insights.

Design, build, and deploy enterprise generative AI and machine learning solutions on AWS using Python. Lead architecture, full-stack development, ML pipelines, RAG systems, AI agents, model evaluation, MLOps, CI/CD, infrastructure automation, container orchestration, security, monitoring, and scalability. Partner across the full SDLC while ensuring production systems meet enterprise governance, compliance, performance, and reliability standards.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

Lead data science and AI consulting engagements for technology, media, and entertainment clients. Develop Agentic AI solutions, statistical models, experiments, KPIs, and advanced analytics to solve business problems. Manage client relationships, teams, staffing, quality assurance, stakeholder communication, and executive presentations. Support proposals, business development, thought leadership, training, and growth of the analytics practice.

Lead end-to-end customer and product analytics projects, analyzing large datasets to identify growth opportunities and business drivers. Apply statistical, predictive, causal, and machine learning methods; develop segmentation, lifecycle, basket, cohort, funnel, and behavioral analyses. Build scalable analytical frameworks, integrate internal and external data, and translate findings into executive-ready recommendations. Partner with cross-functional stakeholders to shape analytical roadmaps and influence business decisions.

Leads data science and analytics for Salesforce Data 360, advising senior executives and cross-functional product leaders. Develops predictive models, metrics, dashboards, data products, and product insights; applies statistical and machine-learning methods to solve complex business problems. Guides team roadmaps, prioritizes impactful work, supports product adoption, and translates analysis into strategic recommendations. Requires advanced quantitative expertise, executive communication, cloud data warehouse SQL, and proficiency in Python or R.

Design and build the production foundation for WGU’s AI student assistant, including retrieval, inference, conversation state, security, privacy, guardrails, evaluation, observability, and service integration. Develop full-stack features in Java and Angular, integrate enterprise systems, support production operations, and mentor engineers. The role requires hands-on LLM application development, authorization-aware data access, and technical leadership across security, reliability, scalability, and cost.

Develop and productionize large language models and agentic AI systems at scale. Design APIs, libraries, container strategies, and LLMOps infrastructure; optimize deep learning training and inference on specialized hardware; and lead AI projects from research and data exploration through implementation and productization. Collaborate with research scientists, engineers, and data scientists across multimodal AI, NLP, computer vision, knowledge representation, and generative AI.

Lead enterprise AI architecture strategy, adoption roadmaps, standards, governance, and responsible AI practices. Partner with technical and business leaders to identify AI opportunities, evaluate platforms, design scalable solutions, and guide cross-functional teams in AI-enabled development, automation, and workflow adoption. Coach architects and developers while ensuring solutions are secure, ethical, compliant, and aligned with business objectives.