You will build and operate AI agents, integrations, and workflows to power the company's GTM platform. This involves managing the full lifecycle of workflows from discovery and prototyping through to deployment and performance measurement.
The Senior FP&A Analyst will support the finance department by managing budgeting, forecasting, and financial reporting processes. They will also build financial models and provide actionable insights to leadership to support strategic business decisions.
Design and implement scalable recommender systems while managing machine learning pipelines for data processing and model deployment. Collaborate with cross-functional teams to integrate models into production and continuously monitor their performance to meet business objectives.
Architect and develop autonomous agentic workflows that enable LLMs to plan, use tools, and perform self-correction. Implement robust state management, error handling, and evaluation frameworks to ensure efficient and safe agent performance.
You will transform complex, multi-source datasets into strategic insights to drive decision-making for global organizations. This involves building data models, visualizations, and collaborating with cross-functional teams to identify patterns and process improvements.
The Enterprise Account Executive will lead the full sales cycle for Fortune 500 and high-growth enterprise clients, focusing on net-new acquisitions and multi-million-dollar deals. They will build trusted advisor relationships with C-level stakeholders and collaborate with internal teams to drive revenue growth and customer success.
The Delivery Manager will own the end-to-end project lifecycle, ensuring high-impact projects are delivered on time, within budget, and to quality standards. They will lead engineering teams, manage client expectations, and drive strategic alignment across cross-functional initiatives.
The Head of Enterprise Account Management will lead the growth of strategic customer accounts while managing and developing the account management team. This role involves driving expansion, increasing net revenue retention, and building deep, multi-year partnerships with enterprise clients.
Architect and implement scalable backend systems that integrate with LLMs and agentic workflows. Manage cloud-native AI applications while ensuring security, performance, and compliance through robust monitoring and CI/CD practices.
Lead technical discovery calls and design ML/AI system architectures to translate client business problems into deliverable solutions. Partner with Account Executives to scope engagements, build technical proposals, and ensure a smooth handoff to delivery teams.
Design, develop, and maintain scalable data pipelines, ETL processes, and data models to support business operations. Collaborate with stakeholders and data scientists to ensure data quality and implement data-driven visualizations.
The MLOps Engineer will deploy machine learning models to production environments and design automated workflows for model training and deployment. They will also manage infrastructure and establish monitoring systems for model performance.
