Top AI & Machine Learning Jobs

Collect, clean, and analyze sports data; develop machine learning and predictive models; create visualizations and reports; collaborate with data and sports strategy teams; and monitor advances in sports analytics.

Founding engineer responsible for architecting and building core product systems, writing scalable Python backend code, designing and maintaining AI/ML infrastructure, instituting best development practices (testing, CI/CD), collaborating on product roadmap, and mentoring future engineers.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Build and scale AI automations, agents, data pipelines, governance, spend dashboards, ROI metrics, and internal AI tooling. Manage Claude access, connector security reviews, AI champions, documentation, training, and executive reporting. Partner across departments to deploy production software, measure adoption and cost, and ensure teams own the tools they use.

Design and build a production AI agent platform with RAG pipelines, document parsing, structured data mapping, and multiple agent types. Deploy and operate ML models on GPU infrastructure while ensuring scalability, security, compliance, observability, and performance in a multi-tenant cloud environment. Collaborate with Product Management and engineering teams, provide technical leadership through design and code reviews, and mentor engineers.

Leads architecture and delivery of production-ready data science, AI, machine learning, forecasting, optimization, and analytics solutions on Azure. Partners with clients, translates business needs into scalable technical designs, guides modeling and deployment, and manages a Senior Data Scientist. Responsibilities include technical leadership, client engagement, model monitoring, stakeholder communication, solution estimation, mentoring, and delivery oversight across complex and ambiguous data environments.

Develops data science, AI, machine learning, NLP, statistical, and visualization solutions for government audit and investigative work. Analyzes structured and unstructured data to identify fraud, waste, and abuse; builds and maintains models and analytical products; uses cloud data environments and relational databases; documents methodologies and technical requirements; communicates findings to stakeholders; and supports training, project collaboration, and agency mission objectives.

Designs and builds enterprise AI agents, Power Platform applications, and automation solutions with business stakeholders. Establishes architecture standards, reusable components, ALM practices, and platform guardrails; owns Dataverse architecture for Dynamics CRM and Dynamics 365 F&O. Leads agent grounding and integration architecture using RAG, MCP, A2A, knowledge sources, enterprise search, connectors, and APIs. Provides technical direction, code reviews, quality standards, and mentorship to a distributed India Center of Excellence.

Leads a portfolio of enterprise applied AI initiatives from strategy and prioritization through production deployment and continuous improvement. Oversees generative AI, agentic AI, machine learning, architecture, evaluation, governance, observability, and responsible AI practices. Manages multidisciplinary teams, establishes reusable platforms and technical standards, guides cloud and technology decisions, and partners with executives, business teams, security, compliance, vendors, and technical stakeholders to deliver reliable, secure, measurable AI capabilities.

Build and deploy AI agents for enterprise customers from discovery through implementation. Map business processes, connect systems, structure data, test and iterate agents, drive adoption, and measure business impact. Collaborate with executives, operators, Customer Success, Education, Product, and Engineering. Travel to customer sites as needed, up to 30%, while developing reusable deployment patterns and product feedback.