Application Development/ Python with GEN AI

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

We are seeking an Applied AI Engineer to join our dynamic team. The ideal candidate will have strong experience in building and operating GenAI systems in production, LLMOps, Python engineering, and retrieval systems and a proven ability to design, deliver, and scale enterprise-grade GenAI workflows and assistants across regulated environments.

Over all 10+ years profiles needed with Strong Python and LLM with GEN AI, * 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 with LLM patterns including RAG, tool or function calling, agentic workflows, and structured outputs.
• LLMOps expertise, including evaluation frameworks, prompt and 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 retrieval systems knowledge, including multi-stage pipelines, vector search, re-ranking, metadata filtering, and evaluation metrics such as recall or precision tradeoffs, MRR, and NDCG.
• Practical experience stabilizing systems through real-world failures such as model regressions, prompt drift, retrieval degradation, and data quality issues.

Preferred Skills:

• 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.