Senior Software Engineer, Juniper Square

• 7+ years of experience designing, building, and operating production software systems
• Strong backend engineering experience with Python frameworks such as FastAPI, Flask, or Django
• Experience building or integrating AI/LLM-powered systems in production - such as RAG pipelines, AI SDKs, evaluation workflows, guardrails, or agentic workflows
• Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling
• Experience with cloud-native technologies such as AWS, Docker, and Kubernetes
• Strong understanding of CI/CD, observability, and operating services in production
• Ability to break down complex technical problems and deliver pragmatic, maintainable solutions
• Strong ownership mindset, with the ability to drive projects independently while collaborating effectively
• Clear communication skills and the ability to explain technical tradeoffs to engineering and cross-functional partners
• Hands-on experience with AI-native development tools (e.g., Cursor, Augment); demonstrated ability to embed AI-driven practices to accelerate velocity and code quality
• Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases

Desirable

• We're hiring Senior Software Engineers to join our AI-native engineering team
• You'll design, build, and operate production AI systems - including AI platform capabilities, agentic workflow infrastructure, and LLM-powered features that serve institutional financial clients
• This is a hands-on individual contributor role for engineers who are deeply fluent in AI-native development and want to work at the intersection of applied AI and backend systems engineering
• Design, build, and ship production-quality backend services, APIs, and AI platform components used across multiple engineering teams
• Build and integrate LLM-powered systems such as RAG pipelines, AI SDKs, evaluation workflows, guardrails, prompt/tool orchestration, and model observability
• Improve the reliability, scalability, observability, and operational quality of production AI systems
• Build internal tools, frameworks, automation, and documentation that improve developer productivity and AI capabilities
• Participate in code reviews, design reviews, debugging, incident response, and operational support
• Contribute to technical design for complex projects, including evaluating tradeoffs and proposing pragmatic implementation plans
• Partner with product, design, and engineering teams to translate platform needs into well-designed technical solutions
• Help identify and reduce technical debt, reliability risks, and friction in the software development lifecycle
• Collaborate with Staff and senior engineers to establish reusable patterns and raise engineering standards
• Use agentic coding tools and LLM-assisted development as a primary part of your workflow — this is how the entire team operates
• Critically evaluate AI-generated code for correctness, edge cases, and regressions — shipping quality output regardless of how it was produced
• Contribute to the team's evolving practices around AI-accelerated development and testi