AI Agent Developer

About the company:

Crosstree is a distinguished boutique investment banking firm focused exclusively on the needs of middle-market companies within three subsectors of the life sciences and healthcare industries: pharmaceutical services, diagnostics and tools, and digital health. Since 2004, Crosstree has advised on more completed transactions in these collective subsectors than any other investment bank. The firm provides clients a full suite of advisory and capital raising services, including both buy-and sell-side mergers and acquisitions advisory, private capital raising, strategic advisory, and valuations. Crosstree's narrow industry focus provides clients the expertise common of a bulge-bracket investment banking firm to middle-market companies and investors, with unparalleled industry insights, well-established strategic relationships across the globe and superior access to capital markets. From our offices in Tampa, Florida, Crosstree has advised clients throughout North America, Europe, India, China, Latin America, and Australia on transactions ranging from $25 million to more than $1 billion.

All securities transactions are offered by and conducted through Crosstree Capital Securities, LLC, a broker-dealer registered with the SEC, a member of FINRA (www.finra.org) and SIPC (www.sipc.org), and an affiliate of Crosstree Capital Partners, Inc.

Candidate Profile:

• We will prioritize candidates with:
• Production AI application experience (beyond prototypes or demos)
• Strong Python and JavaScript proficiency
• Experience working with LLM APIs and orchestration workflows
• API integration and backend systems experience
• RAG/vector search/retrieval architecture exposure
• Strong independent execution and startup ownership mentality
• Fluent English communication skills

The primary focus areas include:

• AI workflow orchestration and API-driven systems
• Production hardening of existing agent pipelines
• Integration between LLMs, proprietary databases, and external services
• Workflow reliability, error handling, monitoring, and scalability
• Customer-facing and internal AI-enabled applications
• Multi-step data synthesis and deliverable generation
• Lightweight frontend/product layer support for MVP development
• The role is less focused on traditional machine learning model development and more focused on applied AI systems engineering and productization.

Key technologies and concepts should include:

• N8N workflow orchestration
• Claude/OpenAI/Gemini APIs
• Supabase/PostgreSQL
• API integrations, webhooks, and OAuth
• RAG pipelines and retrieval workflows
• MCP concepts and agent tooling
• React/Next.js exposure for lightweight frontend support