The ad says“Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django”
The ad says“Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django”
The ad says“Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django”
The ad says“Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django”
The ad says“Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django”
Values
The ad says“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)”
The ad says“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)”
The ad says“Experience with cloud-native technologies such as AWS, Docker, and Kubernetes, and infrastructure as code”
The ad says“Strong data warehousing or data engineering expertise (Redshift, Snowflake, BigQuery, Databricks)”
The ad says“Strong understanding of CI/CD, observability, and operating services in production”
The ad says“Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling”
The ad says“Strong data warehousing or data engineering expertise (Redshift, Snowflake, BigQuery, Databricks)”
The ad says“Experience with cloud-native technologies such as AWS, Docker, and Kubernetes, and infrastructure as code”
The ad says“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)”
The ad says“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)”
The ad says“Experience with cloud-native technologies such as AWS, Docker, and Kubernetes, and infrastructure as code”
The ad says“Experience with cloud-native technologies such as AWS, Docker, and Kubernetes, and infrastructure as code”
The ad says“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)”
The ad says“Strong understanding of CI/CD, observability, and operating services in production”
The ad says“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)”
The ad says“Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling”
The ad says“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)”
The ad says“Comfort working full-stack when the problem calls for it (TypeScript/React experience a plus)”
The ad says“Strong data warehousing or data engineering expertise (Redshift, Snowflake, BigQuery, Databricks)”
The ad says“Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling”
The ad says“Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling”
The ad says“Strong data warehousing or data engineering expertise (Redshift, Snowflake, BigQuery, Databricks)”
The ad says“Comfort working full-stack when the problem calls for it (TypeScript/React experience a plus)”
As a Senior Software Engineer at Juniper Square, you’re a hands-on individual contributor who designs, builds, and operates production systems powering core workflows for institutional financial clients
You bring deep technical expertise to your team, drive complex projects independently, and collaborate closely with Staff engineers, product, design, and QA to ship reliable, well-designed systems
You’ll join Dex, Juniper Square’s AI-native data warehouse, a customer-facing data product
You’ll work on data extraction, built on the existing architecture and running an Ingest → Parse → Classify → Extract pipeline, owning the initial MVP agents and covering structured data extraction, a RAG pipeline, vectorized storage, and a docs repo
Design, build, and ship production-quality systems, APIs, and services - from foundational platform capabilities to customer-facing product experiences
Own projects end-to-end: translating requirements into technical designs, implementation, testing, deployment, and ongoing iteration
Build reliable, scalable, and observable systems that deliver value to customers and enable other engineering teams to move faster
Own the production health of the systems you build, including monitoring, troubleshooting, performance, reliability, and issue resolution
Build intuitive, performant experiences that solve meaningful customer problems and support business outcomes
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, deliberately choosing where AI versus traditional software is the right tool
Critically evaluate AI-generated code for correctness, edge cases, and regressions, shipping quality output regardless of how it was produced
Build or integrate AI/LLM-powered capabilities in your domain where they add real value (e.g., evaluation frameworks, RAG pipelines, agentic workflows), with clear human-in-the-loop boundaries
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 in Dex, with a deep understanding of the data, workflows, and customer needs specific to that space
Preferred experience
Strong ownership mindset, with the ability to drive projects independently while collaborating effectively across teams
Comfort working full-stack when the problem calls for it (TypeScript/React experience a plus)
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)
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
Bachelor’s degree in Computer Science or equivalent work experience
Ability to break down complex technical problems and deliver pragmatic, maintainable solutions
Experience with relational/NoSQL databases, including schema design, query optimization, and data modeling
7+ years of experience in backend and/or full-stack software engineering
Clear communication skills, with the ability to explain technical tradeoffs to engineering and cross-functional partners
Comfort operating in ambiguous, fast-evolving technical territory where best practices and patterns are still being established
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
Strong backend engineering experience with Python and frameworks such as FastAPI, Flask, Pyramid, or Django
If you think this role sounds like a fit, we encourage you to apply even if you don’t meet every qualification
Experience shipping AI-forward data pipeline or warehousing solutions
Strong data warehousing or data engineering expertise (Redshift, Snowflake, BigQuery, Databricks)
Comfort navigating ambiguity in an early-stage, less spec-heavy environment
