Top AI & Machine Learning Jobs

This role involves designing, developing, and maintaining AI/ML products, collaborating with stakeholders, and ensuring model deployment on AWS. Responsibilities include creating APIs, establishing MLOps practices, and driving AI strategy for operational improvements.

Build backend infrastructure for an AI revenue engine. Integrate large language models, optimize data pipelines, and develop scalable systems supporting autonomous revenue workflows for companies in the physical economy.

Design, build, and deploy AI-powered features for a digital banking platform, including natural language interfaces, assistants, and automated workflows. Integrate LLMs and AI APIs using prompt engineering, tool calling, validation, and safety guardrails. Evaluate AI outputs for accuracy, reliability, and regulatory compliance; collaborate cross-functionally; document best practices; conduct code reviews; and assess emerging AI tools and frameworks.

Supports financial services customers in deploying AI risk evaluations and guardrails. Translates laws, regulations, and industry guidance into practical controls; designs, documents, tests, and validates guardrails; partners with product and technology teams on user experience; tracks roadmap objectives and performance; and organizes industry roundtables and customer workshops.

Designs, develops, deploys, and operates AI capabilities for enterprise and facilities-management platforms. Responsibilities include building intelligent assistants, RAG and semantic search solutions, document intelligence workflows, AI APIs, data pipelines, prompt and retrieval strategies, and evaluation frameworks. The role also covers Azure cloud deployment, CI/CD, MLOps, monitoring, security, governance, and responsible AI practices.

Lead AI initiatives for Human Capital by identifying high-impact use cases, designing Python-based prototypes, integrating AI features with product and engineering teams, evaluating AI tools, shaping AI strategy, and mentoring junior engineers. The role requires translating business needs into practical solutions and collaborating across technical and non-technical stakeholders.

Own and develop a production recommendation engine for Kubernetes resource optimization. Design time-series forecasting models, data-quality pipelines, safety guardrails, regression tests, and production metrics for CPU, memory, GPU, and JVM workloads. Investigate customer issues, improve recommendation accuracy, maintain Python services, and guide ML strategy while collaborating with platform engineering, product, and leadership.

Lead AI-driven process improvement for Source-to-Pay: design and deploy GenAI agents, configure Coupa procurement modules (CLMA, Sourcing, Supplier Management), coordinate with Coupa support, test and document configurations, review releases, track defects, and mentor team members.

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 LLM-powered research and analysis products for political strategists. Partner with users to shape workflows, define quality standards, design evaluations and LLM-as-judge systems, prototype features, and communicate technical tradeoffs to technical and nontechnical stakeholders. The role requires strong Python, SQL, statistical and machine learning skills, hands-on LLM and embedding experience, product judgment, and comfort with ambiguous problems.