Senior Software Engineer, AI Platform

Juniper Square•,

1d•$175,000 - $220,000•Hybrid

About The Position

Juniper Square is seeking a Senior Software Engineer to join their AI Platform team. This role is a hands-on individual contributor responsible for designing, building, and operating production systems that power core workflows for institutional financial clients. The AI Platform team builds the foundational infrastructure layer for all AI work at Juniper Square, including AI Gateway & Infrastructure, Governance & Security, Retrieval & Data Access, and Agent Framework. The company culture emphasizes ambitious, meaningful work, ownership, urgency, collaboration, and transparency. Juniper Square offers flexible work arrangements, including remote and in-office options, with a global team presence.

Requirements

7+ years of experience in backend and/or full-stack software engineering
Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django
Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling
Experience with cloud-native technologies such as AWS, Docker, and Kubernetes, and infrastructure as code
Strong understanding of CI/CD, observability, and operating services in production
Comfort working full-stack when the problem calls for it (TypeScript/React experience a plus)
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 across teams
Clear communication skills, with the ability to explain technical tradeoffs to engineering and cross-functional partners
Hands-on experience with AI-native development tools (e.g., Cursor, Augment), and demonstrated AI Fluency: a deliberate point of view on when AI is (and isn't) the right tool, adapted code review practices for AI-generated code, and safeguards against over-reliance on AI tooling
Bachelor's degree in Computer Science or equivalent work experience
Hands-on experience building or integrating AI/LLM-powered systems in production (e.g., RAG pipelines, agent frameworks, evaluation workflows, guardrails, prompt/tool orchestration, or model observability)
Comfort operating in ambiguous, fast-evolving technical territory where best practices and patterns are still being established

Nice To Haves

Background in financial services or fintech
Experience with API gateway/infrastructure, security & governance tooling, retrieval and data-access systems, or agent frameworks at production scale

Responsibilities

Design, build, and ship production-quality systems, APIs, and services.
Own projects end-to-end, from requirements translation to technical design, implementation, testing, deployment, and iteration.
Build reliable, scalable, and observable systems that deliver customer value and enable other engineering teams.
Own the production health of systems, including monitoring, troubleshooting, performance, reliability, and issue resolution.
Build reusable infrastructure, frameworks, tooling, and developer experiences.
Improve engineering velocity and operational quality through automation, documentation, and continuous improvements.
Contribute to technical design for complex projects, evaluating tradeoffs and proposing pragmatic implementation plans.
Partner with product, design, and engineering to translate requirements 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.
Participate in code reviews, design reviews, debugging, incident response, and operational support.
Use agentic coding tools and AI-assisted development as a primary part of your workflow.
Critically evaluate AI-generated code for correctness, edge cases, and regressions.
Build or integrate AI/LLM-powered capabilities where they add real value.
Contribute to the team's evolving practices around AI-accelerated development, testing, and code review.
Work closely with product, design, QA, and cross-functional partners to meet timelines and align solutions to business goals.
Provide clear, accessible technical context to both technical and non-technical stakeholders.
Grow into a subject-matter expert with a deep understanding of the data, workflows, and customer needs specific to the AI Platform space.