Staff Software Engineer, AI

Private markets are one of the largest, most complex, and most underserved corners of global finance. Our mission at Juniper Square is to unlock their full potential. We’re the Operations Partner trusted by 2,300+ GPs, unifying technology, data, and fund administration services into a single platform that helps GPs move faster, make better decisions, and scale with precision. With $300B+ under administration and 700,000+ LPs on platform, we’ve built the scale to match our ambition. And with JunieAI, our purpose-built AI platform, we’re reimagining how private markets operate, embedding intelligence across every workflow. Founder-led since 2014, backed by $350M+ in funding, and now 1,000+ employees strong, we’re building a company designed to shape the future of private markets for decades to come.

Our culture is built for people who want to do ambitious, meaningful work alongside exceptionally talented teammates. We think like owners, move with urgency, and take pride in solving hard problems that truly matter to our customers and the future of private markets. We believe the best ideas come from open debate, deep collaboration, and diverse perspectives, which is why we believe transparency is the default and feedback makes us stronger. If you’re energized by high standards, rapid growth, and the opportunity to help define a category at a pivotal moment, come join us!

Juniper Square offers employees a variety of ways to work, ranging from a fully remote experience to working full-time in one of our physical offices. We invest heavily in digital-first operations, allowing our teams to collaborate effectively across 27 U.S. states, 2 Canadian Provinces, India, Luxembourg, and England. We also have physical offices in San Francisco, New York City, Mumbai and Bangalore for employees who prefer to work in an office some or all of the time.

As a Staff Software Engineer at Juniper Square, you will be primarily a technical leader, identifying and acting on opportunities to increase the engineering team's efficiency, stability, and consistency. You will lead an engineering team and collaborate closely with product, design, and QA teams to build and deliver delightful user experiences that make complex workflows simple and intuitive.

You will lead the team building Juniper Square's AI-powered document intelligence platform. You will own the technical strategy for structured document extraction — transforming unstructured financial documents (K-1s, capital call notices, subscription agreements, fund reports) into structured, queryable data at scale. You will also lead the architecture and ongoing evolution of our in-house RAG pipeline, enabling intelligent retrieval and generation over private-markets documents. This is a high-impact role at the intersection of applied AI, backend systems, and data engineering, where your work directly powers core product experiences for our customers.

• Bachelor's degree in Computer Science, Mathematics, AI/ML, or a related technical field.

• 7+ years of backend and/or ML engineering experience, with a trajectory of increasing technical leadership, architectural responsibility, and mentorship.

• Deep expertise in Python, with strong proficiency in building production-grade backend services and data pipelines; experience with other server-side languages (Node/TS, Java) a plus.

• Solid understanding of Python web frameworks (like Django or FastAPI)

• Hands-on experience designing and operating document processing pipelines, including parsing, extraction, classification, and structured output generation from unstructured documents (PDFs, scanned files, financial forms, etc.)

• Production experience building and operating RAG systems, including chunking strategies, embedding models, vector stores (e.g., pgvector, Pinecone, Weaviate), retrieval, and reranking

• Experience evaluating and improving LLM-based extraction quality — including designing eval frameworks, handling edge cases, and building human-in-the-loop feedback mechanisms

• Familiarity with model serving, inference optimization, and managing LLM API costs at scale

• Experience with LLM application patterns beyond basic RAG — including tool-calling agents, planning/execution loops, and multi-step reasoning systems — and how these apply to document intelligence workflows

• Experience with Relational Databases like Postgres or MySQL

• Experience with Cloud technologies (AWS preferred) and Container technologies (Docker and k8s)

• Deep understanding of service-oriented architecture, modern software development practices, and developing scalable, reliable systems

• Ability to identify and evaluate opportunities to integrate AI capabilities into products and workflows

• Demonstrable product focus and a keen understanding of how technology can solve customer problems and drive business outcomes

• Highly self-driven, with a proactive approach to leadership, technical problem-solving, and initiative execution

• Experience working in agile development environments and familiarity with practices that promote rapid iteration and velocity

• Excellent communication and collaboration skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders and align them on product goals

• Proven ability to lead projects end-to-end with a player-coach mindset — hands-on in code and architecture while working autonomously with product partners

• Ability to manage multiple priorities and lead teams effectively in a fast-paced environment, with the flexibility to adapt as needs shift

• Demonstrated track record of mentoring engineers and elevating team technical capability

• Hands-on experience with AI-native development tools (e.g., Cursor, Augment, Loveable); demonstrated ability to embed AI-driven practices to accelerate team velocity and code quality

• Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases introduced by AI-assisted development

• Experience building and shipping production-grade software using AI-assisted workflows across the full SDLC

• Experience with OCR technologies and document understanding models (e.g., AWS Textract, Azure Document Intelligence, LayoutLM, Donut)

• Background in financial document processing or fintech data pipelines

• Experience with MLOps tooling (experiment tracking, model registries, deployment pipelines)

• Familiarity with evaluation frameworks for LLM/extraction quality (e.g., RAGAS, custom evals, human review pipelines)

• Experience with multi-modal models or vision-language models for document understanding

• Knowledge of data privacy and compliance considerations in document processing pipelines (PII handling, encryption, access controls)