Applied AI Engineer
- Suncap Technology
5 Days Ago
NA
C2C, 1099
New York-NY
5-10 Years
Required Skills: GenAI, Python, Java, RAG, LLMops, AI data ingestions pipelines
Job Description
Overview:
Team is building an enterprise grade GenAI workflow platform to enable document data extraction, embedded productivity assistants, and automated business workflows across 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 understand failure modes, evaluation, and governance as first-class AI-powered systems.
Team is building an enterprise grade GenAI workflow platform to enable document data extraction, embedded productivity assistants, and automated business workflows across 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 understand failure modes, evaluation, and governance as first-class AI-powered systems.
What You'll Do
- Design and evolve reusable GenAI workflows used across Lending business lines.
- Develop an enterprise grade AI-based document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.
- Build AI-powered assistants embedded in Lending systems using agentic workflows.
- Deliver automated content and deck generation workflows for reporting and approvals.
- Provide expert advice on GenAI architecture including model selection, orchestration patterns, and evaluation strategy.
- Establish LLMOps practices: extraction accuracy, assistant reliability, prompts management, and audit monitoring.
- Design and implement controls for entitlements, PII handling within open-source models in a regulated environment.
- In the role you are expected to act as a hands-on technical expert, and it has a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.
What You'll Bring
- 2+, dedicated experience in practical application of GenAI solutions in an enterprise business environment. Designing and operating GenAI orchestration frameworks in production beyond vendor examples (e.g., LangChain systems).
- 5+ years of strong front-to-back engineering experience, focusing on AI/ML platforms and workflows (Python or Java).
- Proven experience building and operating production-grade GenAI / LLM platforms, applying patterns such as RAG, tool/function calling, agentic workflows, and validated structured outputs.
- Strong LLMOps expertise, including evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production systems.
- Hands-on experience building AI-Financial ingestion pipelines with measurable quality, accuracy, and reliability.
- Advanced retrieval experience: advanced vector search, including multi-vector and late-interaction approaches (e.g., ColBERT, chunking), multi-stage retrieval pipelines, metadata filtering, re-ranking. Solid understanding of evaluation metrics and how they shape practical RAG system design (e.g., recall vs precision, latency vs quality, MRR, NDCG).
- Experience operating GenAI systems through real production failures (model regressions, retrieval degradation, prompt drift, data quality issues) and designing mitigation strategies.
Nice to Have
- Fixed Income or Institutional Lending domain experience.
- Experience working in regulated environments with strong audit and control requirements.
- Familiarity with enterprise security, data governance, and entitlement models.
- Experience designing reusable internal platforms or shared developer tooling.
- Frontend experience is beneficial (Angular or React).
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
