Staff Software Engineer, AI

Juniper Square•,

1d•Hybrid

About The Position

Juniper Square is a leading operations partner for private markets, offering a unified platform for technology, data, and fund administration. With a mission to unlock the potential of private markets, the company utilizes its AI-powered platform, JunieAI, to embed intelligence across workflows. Founded in 2014 and backed by significant funding, Juniper Square has grown to over 1,000 employees and operates across multiple countries, with physical offices in San Francisco, New York City, Mumbai, and Bangalore. The company fosters a culture of ambitious, meaningful work, driven by ownership, urgency, open debate, and diverse perspectives. They emphasize transparency and continuous improvement, attracting individuals energized by high standards, rapid growth, and category definition.

Requirements

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.
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.
Production experience building and operating RAG systems, including chunking strategies, embedding models, vector stores, 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.
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.
Proven ability to lead projects end-to-end with a player-coach mindset.
Ability to manage multiple priorities and lead teams effectively in a fast-paced environment.
Demonstrated track record of mentoring engineers and elevating team technical capability.
Hands-on experience with AI-native development tools (e.g., Cursor, Augment, Loveable).
Ability to critically evaluate AI-generated code and outputs.
Experience building and shipping production-grade software using AI-assisted workflows across the full SDLC.

Nice To Haves

Experience with other server-side languages (Node/TS, Java) a plus.
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).

Responsibilities

Serve as a technical leader, identifying and acting on opportunities to increase engineering team efficiency, stability, and consistency.
Lead an engineering team in close collaboration with product, design, and QA teams to build and deliver user experiences.
Lead the team building Juniper Square's AI-powered document intelligence platform.
Own the technical strategy for structured document extraction, transforming unstructured financial documents into structured, queryable data at scale.
Lead the architecture and ongoing evolution of the in-house RAG pipeline for intelligent retrieval and generation over private-markets documents.
Champion and embed AI-native development practices and tools to achieve significant productivity gains.
Take ownership over the team's architecture, actively participating in design reviews and driving the long-term technical vision.
Contribute actively to coding, testing, and delivering roadmap projects, writing high-quality, well-tested, secure, and maintainable code.
Design and implement document processing pipelines, including extraction, classification, chunking, embedding, and retrieval, leveraging LLMs and AI tooling.
Own the end-to-end design and reliability of document extraction and RAG pipelines, defining quality benchmarks and evaluation frameworks.
Effectively manage the team's short-term roadmap, identifying risks and creating mitigation strategies.
Provide backend and AI/ML-focused technical leadership, mentoring engineers and helping build high-performance teams.
Collaborate with cross-functional partners to ensure project timelines and solutions align with business strategy.
Own monitoring, diagnosing, and resolving production issues within the team's services.
Conduct code reviews and participate in architecture and system design discussions.
Implement and ensure best practices across teams to maximize developer productivity.
Actively seek opportunities to improve platform and developer experience and own those initiatives.
Partner with recruiting to build and grow the team.
Grow into a subject matter expert (SME) in AI document extraction and RAG systems.