Title Applied AI Engineer GenAI / Full Stack
Location-Hybrid @ NYC-Need Locals Only
Duration Contract
Interview: In Person Client Interview
Skills Needed: +5 front and backend GenAI development and engineering with Python or Java, +2 years application of GenAI solutions in an enterprise business enviroment, RAG, deployment and production support, LLMops, AI data ingestions pipelines, etc.
Glider Assessment (Y/N): Python Glider
Overview
Our Fixed Income Institutional Lending Technology team is building an enterprise-grade GenAI workflow platform enabling document data extraction, embedded productivity assistants, and automated business workflows across Lending business lines.
This is not a research or demo role. We are seeking senior, hands-on full-stack engineers who have designed, built, and operated GenAI systems in production and who treat failure modes, evaluation, and governance as first-class concerns. The role is a hands-on technical expert seat with a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.
What You'll Do
- Design and evolve reusable GenAI workflows used across Lending business lines.
- Build an enterprise-grade AI document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.
- Develop AI-powered assistants embedded in Lending systems using agentic workflows.
- Deliver automated content and deck generation workflows for reporting and approvals.
- Advise on GenAI architecture: model selection, orchestration patterns, and evaluation strategy.
- Establish LLMOps practices covering extraction accuracy, assistant reliability, prompt management, and audit monitoring.
- Design and implement controls for entitlements and PII handling, including safe use of open-source models in a regulated environment.
What You'll Bring
- 6-7+ years of front-to-back engineering experience in Python or Java, with a focus on AI/ML platforms and workflows.
- 3+ years of dedicated, practical GenAI experience in an enterprise business environment, including designing and operating orchestration frameworks in production beyond vendor examples (e.g., custom LangChain-based systems).
- Proven experience building and operating production-grade GenAI/LLM platforms applying RAG, tool/function calling, agentic workflows, and validated structured outputs.
- Strong LLMOps expertise: evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production.
- Hands-on experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability.
- Advanced retrieval depth: multi-vector and late-interaction approaches (e.g., ColBERT), chunking strategy, multi-stage retrieval pipelines, metadata filtering, and re-ranking plus a working command of evaluation metrics (recall vs. precision, latency vs. quality, MRR, NDCG) and how they shape RAG design.
- Experience operating GenAI systems through real production failures model regressions, retrieval degradation, prompt drift, data quality issues and designing mitigations.
Nice to Have
- Fixed Income or Institutional Lending domain experience.
- Experience in regulated environments with strong audit and control requirements.
- Familiarity with enterprise security, data governance, and entitlement models.
- Experience building reusable internal platforms or shared developer tooling.
- Frontend experience (Angular or React).
Regards,
Sai Srikar
Email:
Never miss an opportunity! Create an alert based on the job you applied for.
