Sr AI​/Agentic Engineer

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• Lead delivery of document intelligence pipelines that read loan applications, tax returns, bank statements and financial statements with human-level comprehension and audit trails.
• Build underwriting copilots that surface risk signals, policy checks and recommended conditions in real time for Lendistry underwriters.
• Develop borrower-facing conversational AI that helps small business owners navigate applications, understand decisions and manage their loans.
• Harden the shared AI platform – prompt registry, tool‑calling framework, evaluation harness, retrieval infrastructure and inference routing layer used by every product team.
• Define and ship the evaluation and observability layer that turns AI reliability into a measurable, managed property of the system.
• Own end‑to‑end LLM features – requirements, design, implementation, evaluation, deployment, production operation – across origination, underwriting, servicing and borrower experience.
• Design new agentic workflows: LLMs that plan, call tools, evaluate results and iterate across complex lending tasks with appropriate human‑in‑the‑loop controls.
• Maintain, debug and improve existing LLM‑powered features in production: prompt pipelines, retrieval systems, document intelligence stack.
• Fine‑tune foundation models (including LLaMA‑family open‑weight and Bedrock‑hosted models) using LoRA, QLoRA, instruction tuning and prompt optimisation to meet Lendistry‑specific tasks.
• Build RAG systems end‑to‑end – chunking, embedding selection, vector retrieval, hybrid search and re‑ranking tuned for financial documents and lending policy.
• Lead development of document processing pipelines that extract structured data from PDFs, scanned images and unstructured financial documents using OCR, layout understanding and LLM‑based extraction.
• Design validation, confidence scoring and fallback mechanisms that make AI outputs safe for regulated, high‑stakes financial decisions, with clear audit trails and escalation paths.
• Diagnose and resolve agentic failure modes – non‑determinism, prompt sensitivity, tool misuse, looping, context‑window exhaustion and retrieval gaps – and build patterns to prevent recurrence.
• Contribute to and shape the shared AI platform – the prompt registry, tool‑calling framework, evaluation harness, retrieval infrastructure, and inference routing layer owned by the AI team.
• Design evaluation frameworks that measure model quality, output reliability, retrieval accuracy and regressions across iterations – golden sets, LLM‑as‑judge scoring, human‑review harnesses.
• Instrument AI systems with observability (logging, metrics, traces, token and cost accounting, drift monitoring, alerting) and manage cost and latency at the feature level – token budgeting, response caching, model‑tier routing and batching strategies.
• Collaborate with product, credit, underwriting and platform engineering to translate business requirements into reliable LLM system designs.
• Mentor junior AI engineers through design reviews, code reviews and pairing.
• Lead proof‑of‑concept work to validate new AI use cases quickly, measure real business impact and scale what works into production.
• Use AI coding assistants (Claude Code, Git Hub Copilot, Cursor or equivalents) as a standard part of the development loop for code generation, refactoring, testing, documentation and review.
• Follow human‑review process: maintain clear judgment and established criteria for when to trust, verify or override AI‑generated suggestions consistent with Lendistry’s AI policies and regulatory requirements.
• Adopt and share emerging agentic development…