Backend Engineer AI

Overview

Staff Backend Engineer responsible for architecting and building foundational systems that power our AI‑native product.

• Architect distributed systems that power AI agent orchestration, handling complex multi‑step workflows with reliability and observability.
• Design and implement core backend services in Golang for high‑performance, concurrent operations and Python for AI/ML integrations and data pipelines.
• Build robust APIs and service interfaces that abstract AI capabilities for consumption by multiple clients and internal systems.
• Own the technical roadmap for backend infrastructure supporting LLM integrations, vector search, and real‑time data processing.
• Establish architectural patterns and standards for service design, data modeling, and inter‑service communication.
• Drive system scalability and reliability, implementing caching strategies, database optimization, and async processing patterns.
• Build data pipelines that process equity data, embeddings, and user interactions to continuously improve AI model performance.
• Collaborate cross‑functionally to translate business requirements into technical architecture and make pragmatic build versus buy decisions.
• Mentor engineers on backend best practices, system design, and technical decision‑making.

• 7+ years of backend engineering experience building production systems at scale.
• Expert‑level proficiency in Python for AI/ML integrations, data processing, and scripting.
• Strong Python skills with deep understanding of concurrency patterns, performance optimization, and production Go services.
• Proven experience designing and building distributed systems, including microservices architecture, event‑driven systems, and async workflows.
• Deep knowledge of API design (REST) and service‑to‑service communication patterns.

• Demonstrated experience building backend systems that integrate LLMs and AI capabilities into production applications.
• Hands‑on work with agent frameworks, LLM orchestration, and AI service reliability.
• Familiarity with RAG architectures, vector databases (Pinecone, Weaviate, Qdrant), and embeddings pipelines.
• Experience building evaluation frameworks and observability for AI systems.
• Understanding of prompt engineering and LLM API optimization (latency, costs, rate limits).

• Track record of architecting greenfield services from concept to production with scalability and maintainability in mind.
• Experience with database design and optimization (Postgre
  
  SQL, Redis, time‑series databases).
• Strong foundation in system observability, monitoring, and debugging distributed systems in production.
• Proven ability to lead technical initiatives and establish engineering best practices.
• Experience mentoring engineers and elevating team technical capabilities.

• Experience with Kubernetes, Docker, and cloud infrastructure (AWS/GCP).
• Background in Fin Tech or regulated industries.
• Contributions to open‑source projects or technical writing.
• Familiarity with AI‑powered developer tools like Cursor or Copilot.

• Competitive salary and equity.
• Medical, Dental, and Vision insurance.
• Unlimited PTO + Winter holiday break.
• Parental leave.
• Generous stipends for WFH, learning, wellness, and AI tools.
• 401(k) match (US) / Pension match (Canada).

$200,000—$240,000 USD