Leads development of AI-powered fintech applications, building scalable backend services, frontend experiences, and GenAI features like agent orchestration and RAG for customer support and integrations. Requires hands-on LLM experience, full-stack skills, and ability to experiment rapidly with user feedback.
228k – 370k/yr
HybridFullstack Engineering
About the role
Responsibilities
Build across the stack: design, develop, and maintain scalable backend services and APIs, as well as intuitive, high-quality frontend applications.
Work with AI engineers, software engineers, and machine learning engineers to architect, design, and implement GenAI-powered products and features.
Collaborate across functions to understand user needs and propose/implement AI-powered solutions with highest impact.
Design and execute rapid experiments to push boundaries on business impact from emerging AI capabilities, focusing on minimal viable testing.
Balance creative exploration with rigorous evaluation of technical feasibility, product potential, and business impact.
Requirements
Experience building backend services and working with microservices or service-oriented architectures.
Strong working knowledge of HTML, CSS, JavaScript, and modern frontend frameworks or libraries.
Hands-on experience working with LLMs to build and ship products, iterating with real user feedback, including:
Prompt engineering
Fine-tuning
Retrieval augmented generation (RAG)
Semantic search - vector database and embedding models
Agent orchestration frameworks
Evaluation and monitoring frameworks for open-ended tasks
Streaming and SSE
Common UX and design patterns for GenAI-powered products
Strong debugging and monitoring experience for production systems.
Ability to deeply understand customer/user needs through user research and rapid experimentation.
Ability to balance divergent and convergent thinking for 0→1 projects.
Extremely curious and passionate about GenAI applications.
Nice-to-Haves
Experience training/serving ML models in production, or fine-tuning LLMs for domain-specific use cases.
Comfortable operating in privacy/PII-sensitive environments with compliance mitigations.
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