Applied AI Engineer

Applied AI Engineer Hybrid 3 days In- person Interview Description 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 Youll 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. Role Overview We are seeking a highly skilled Senior GenAI Engineer to design, build, and operate enterprise-grade Generative AI platforms. This role requires hands-on expertise in GenAI orchestration frameworks, LLMOps practices, and advanced retrieval systems, with a focus on delivering reliable, scalable, and production-ready AI solutions. Key Responsibilities Design and implement GenAI orchestration frameworks for enterprise-scale applications. Build and maintain production-grade LLM platforms with robust reliability and observability. Develop advanced retrieval pipelines and optimize vector search strategies. Establish LLMOps practices including evaluation, regression testing, and prompt management. Collaborate with cross-functional teams to integrate GenAI solutions into business workflows. Troubleshoot and mitigate production issues related to GenAI systems. What You’ll Bring 2+ years of dedicated experience applying GenAI solutions in enterprise environments. Proven ability to design and operate GenAI orchestration frameworks in production (e.g., LangChain systems). 5+ years of engineering experience with AI/ML platforms and workflows (Python or Java). Hands-on 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: evaluation harnesses, prompt/version management, regression testing, observability, and reliability measurement. Experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability. Advanced retrieval expertise: vector search (multi-vector, late interaction approaches like ColBERT, chunking), multi-stage retrieval pipelines, metadata filtering, reranking. Solid understanding of evaluation metrics (recall vs precision, latency vs quality, MRR, NDCG) and their impact on RAG system design. Experience operating GenAI systems through real production failures (model regressions, retrieval degradation, prompt drift, data quality issues) and designing mitigation strategies. Nice to Have Domain experience in Fixed Income or Institutional Lending. 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 development experience (Angular or React) #J-18808-Ljbffr LanceSoft