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Our mission is to unlock the full potential of private markets. Privately owned assets like commercial real estate, private equity, and venture capital make up half of our financial ecosystem yet remain inaccessible to most people. We are digitizing these markets, and as a result, bringing efficiency, transparency, and access to one of the most productive corners of our financial ecosystem. If you care about making the world a better place by making markets work better through technology – all while contributing as a member of a values-driven organization – we want to hear from you.
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.
About your role:We are seeking a highly skilled Technical Lead for AI Development to drive the architecture, design, and execution of advanced AI systems using LLM frameworks, multi-agent architectures, RAG pipelines, and Model Context Protocol (MCP) integrations. The ideal candidate has strong hands-on experience building production-grade AI features, orchestrating agent ecosystems, evaluating model performance, and iterating through continual refinements.
You will lead a team of engineers, collaborate with product and research teams, and play a key role in shaping our AI strategy and platform capabilities.
Key Responsibilities:AI Architecture & Development
Design and implement multi-agent systems, including agent orchestration, delegation, and tool interaction patterns.
Build scalable RAG (Retrieval-Augmented Generation) architectures using vector databases, embedding pipelines, and data chunking strategies.
Integrate and extend MCP (Model Context Protocol) tools for robust model-tool communication and workflow automation.
Lead development of AI-based features, prototypes, and production solutions using LLM APIs or self-hosted models.
Architect and optimize prompt engineering, prompt chains, agent loops, and refinement pipelines.
Model Evaluation & Continuous Improvement
Implement and maintain agent evaluation frameworks (agent evals, scenario tests, regression testing).
Design automated evaluation harnesses for LLM quality, reliability, hallucination control, and performance metrics.
Drive iterative improvements through A/B testing, reward models, and feedback loops.
Monitor system performance, latency, cost, and reliability — and implement optimization strategies.
Technical Leadership
Lead and mentor engineers working on AI, data, and backend components.
Collaborate with product managers, researchers, and cross-functional teams to align tech strategy with business outcomes.
Conduct code reviews, enforce best practices, and maintain architectural standards.
Own technical roadmaps, sprint planning, and engineering execution.
Systems & Infrastructure
Work with cloud platforms (AWS/GCP/Azure) to deploy scalable AI services.
Integrate vector databases (Pinecone, Weaviate, Elasticsearch, etc.).
Build APIs and microservices to expose AI capabilities to internal and external stakeholders.
Maintain secure, compliant, and efficient data pipelines for ingestion and retrieval.
Bachelor’s/Master’s degree in Computer Science, Engineering, AI, or related field.
8+ years of software engineering experience with strong backend architecture skills.
3+ years deep experience with LLMs, GPT models, agents, or advanced ML systems.
Strong hands-on experience with:
MCP tools and LLM tool integration
Agent frameworks (e.g., OpenAI Agents, LangChain, LlamaIndex, custom agents)
RAG pipelines, embedding models, vector stores
Agent evaluation, reliability testing, and model refinements
Proficiency in Python, TypeScript/Node.js, or similar languages.
Experience deploying LLM apps and APIs in production environments.
Deep understanding of AI limitations, hallucination control, and safety measures.
Experience with:
Fine-tuning LLMs
OpenAI API, Claude, or Azure OpenAI
Distributed embeddings and high-throughput retrieval systems
MLOps frameworks
Knowledge of DevOps, CI/CD, containerization (Docker/Kubernetes).
Prior leadership experience managing small to mid-size engineering teams.
