eloquentai - Lead Software Engineer
Requirements
• 8+ years of hands-on experience building full-stack production applications. • Prior experience leading or mentoring engineers. • Proficiency in React, TypeScript, and Node.js. • Strong knowledge of cloud infrastructure (AWS, GCP, or Azure) and scalable architectures. • Understanding of AI-powered applications (LLMs, chat interfaces, agentic workflows). • Ability to work in a fast-paced, high-autonomy environment. • Strong collaboration skills across engineering, product, and AI teams. • You have experience building AI-powered applications with LLM integrations. • You’ve worked in high-performance startups or enterprise AI environments. • You have a sharp eye for UI/UX design and have built intuitive, AI-driven interfaces. • You have experience with GraphQL, WebSockets, or real-time data streaming. • You’ve contributed to open-source projects or have built developer tools for AI.
Responsibilities
• As a Lead Engineer at Eloquent AI, you will lead the development of AI-powered full-stack applications while overseeing and mentoring other engineers. You’ll remain hands-on across the stack, but also take ownership of technical direction, code quality, and delivery standards. • You’ll work closely with engineers, AI researchers, and product teams to ensure scalable, reliable systems that power real-time AI-driven workflows. This role requires strong engineering fundamentals, leadership capability, and the ability to operate effectively in a fast-paced, AI-first environment. • Design and build full-stack applications that power AI-driven workflows for enterprise users. • Oversee and review the work of other engineers, ensuring high-quality, production-ready code. • Provide technical guidance, architectural direction, and hands-on support where needed. • Develop high-performance front-end interfaces for AI agent control, monitoring, and visualisation. • Build scalable backend services that support real-time AI interactions, knowledge retrieval, and automation. • Work closely with AI researchers and ML engineers to integrate LLMs, RAG, and automation into production-ready systems. • Establish engineering best practices across testing, deployment, and performance optimisation. • Continuously iterate and refine AI-driven products, balancing speed with robustness.
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