Applied AI Engineer - New York

Job Description:

Applied AI Engineer

What You ll Do

• Design and evolve reusable GenAI workflow primitives and services used across Institutional Securities workflows.
• Develop AI-powered assistants embedded into core Institutional Securities applications, leveraging agentic and tool-driven workflows.
• Define and guide GenAI architecture decisions, including model selection, orchestration patterns, and evaluation strategies.
• Establish and evolve LLMOps practices, including evaluation harnesses, prompt/version management, monitoring, and regression testing.
• Design and implement controls for entitlements, data security, and PII handling, including usage of open-source models in regulated environments.
• Partner with business and platform teams to drive adoption of shared GenAI capabilities across systems and workflows.

What You ll Bring

• 2+ years of hands-on experience building and operating GenAI systems in production
• 7+ years of full-stack or platform engineering experience, with strong proficiency in Python.
• Proven experience designing and operating LLM-based systems using patterns such as RAG, tool/function calling, agentic workflows, and structured outputs.
• Strong expertise in LLMOps, including evaluation frameworks, prompt/version management, regression testing, observability, and production reliability.
• Experience building AI-first document ingestion and extraction pipelines with measurable quality and accuracy.
• Experience with coding agents (Claude code, Codex, AMP, CoPilot)
• Advanced experience in retrieval systems, including multi-stage pipelines, vector search, re-ranking, metadata filtering, and evaluation metrics (e.g., recall/precision tradeoffs, MRR, NDCG).
• Practical experience debugging and stabilizing systems through real-world failure scenarios, including model regressions, prompt drift, retrieval degradation, and data quality issues.

Nice to Have: -

Experience in Fixed Income, Credit, or broader Institutional Securities workflows.

Familiarity with enterprise data governance, security models, and entitlements frameworks.

Experience designing reusable internal platforms or shared developer tooling.