AI operators automating regulated financial workflows
Eloquent AI builds autonomous AI agents that automate complex, regulated customer operations like onboarding, fraud reviews, and compliance workflows in financial services. They serve banks, fintechs, and insurance firms, enabling action-taking AI without APIs or engineering by learning from SOPs. This reduces costs, ensures compliance via auditable actions, and scales 24/7 service.
Leads development of AI-powered full-stack applications, oversees engineers, and integrates LLMs/RAG for enterprise workflows. Requires 8+ years experience with React, TypeScript, Node.js, Python, and cloud infrastructure.
Builds and scales full-stack applications powering AI-driven workflows, including high-performance front-end interfaces and backend services for real-time AI interactions. Requires 7+ years experience with React, TypeScript, Node.js, Python, cloud infrastructure, and AI integrations.
Builds intuitive UIs, CUI designs, agent dashboards, and visualization tools for AI-powered enterprise applications. Requires 3+ years front-end experience with React, TypeScript, design systems, and strong UX focus.
Designs AI-powered dashboards, intuitive data visualizations, and conversational UIs for enterprise AI products. Collaborates with engineers, PMs, and AI researchers to create seamless user experiences in fast-paced environments. Requires 5+ years in product design with strong Figma proficiency.
Builds full-stack applications powering AI-driven workflows, including front-end interfaces for agent control and scalable backends for real-time AI interactions. Requires 1-2 years full-stack experience with React, TypeScript, Node.js, Python/Go, and cloud infrastructure.
Builds and optimizes multimodal LLM-based AI agents for enterprise conversations, integrating with systems via APIs and automating high-stakes workflows. Requires 3+ years in AI engineering, Python/PyTorch proficiency, and experience with LLMs or vision models.
Designs and builds scalable AI infrastructure for deploying enterprise AI agents, automates LLMOps/MLOps workflows, optimizes GPU workloads, and ensures production reliability using Kubernetes and cloud platforms. Requires 5+ years in MLOps/infrastructure and deep expertise in Python and distributed systems.
Builds, deploys, and optimizes enterprise-grade AI agents for high-stakes conversations, integrating with enterprise systems and fine-tuning LLMs. Requires 3+ years in AI engineering or NLP with Python, PyTorch/TensorFlow proficiency.
12-week fellowship for PhD/MSc STEM graduates to gain hands-on experience in AI engineering, product development, and research. Fellows develop/deploy AI agents using LLMs/RAG, optimize models, and work on enterprise projects with mentorship from AI leaders.
Leads end-to-end deployment of AI agents into enterprise environments, scopes customer use cases, configures and tests systems, and manages incidents via on-call rotation. Requires 3-5 years in SaaS implementation and hands-on LLM experience.