Applied AI Engineer

Software Guidance & Assistance, Inc., (SGA), is searching for an Applied AI Engineer for a CONTRACT assignment with one of our premier Financial Services clients in New York, NY.
The firm's Fixed Income Credit Complex team is building an enterprise-grade GenAI workflow platform to power document intelligence, embedded productivity assistants, and automated business workflows across Institutional Securities. This is a production engineering role, not a research or prototype track. We are seeking senior, hands-on engineers who have designed, built, and operated GenAI systems in production environments. This role contributes to a shared platform used across business lines, with a clear path to owning core GenAI capabilities and standards within Institutional Securities.
Responsibilities :

• 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.

Required Skills :

• 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.

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.

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