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# Senior Software Engineer, Backend - Financial AI Product

- **Company:** CO-RIPPLING LLC
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $168,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Information Extraction, Software Product Management, Model-Driven Development, Data Ingestion, Large Language Models, Backend
- **Published:** October 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=29bbfbb1aa992489

## About the Role

* Strong software engineering fundamentals and experience building production systems at meaningful scale.
 * Experience designing backend systems, data models, APIs, workflows, or platform infrastructure for complex business domains.
 * Strong judgment around reliability, correctness, edge cases, and operational risk.
 * Interest in applied AI, LLMs, agents, evals, retrieval, structured extraction, or AI-assisted workflows.
 * Ability to reason clearly about where AI should be used, where deterministic systems should take over, and how to build guardrails around model-driven workflows.
 * Product sense and comfort operating in ambiguous problem spaces where the right answer is not obvious upfront.
 * Ability to partner with product managers, domain experts, operations teams, and engineers to turn messy real-world workflows into simple, scalable software.
 * A high-ownership mindset: you can take a problem from vague customer pain through architecture, implementation, rollout, measurement, and iteration.

## Description

* Build AI-powered product capabilities for complex financial workflows such as onboarding, data ingestion, reconciliation, validation, exception handling, and operational review.
 * Design systems that combine model-driven reasoning with deterministic financial execution.
 * Build workflows that trace financial values back to source evidence, surface unresolved gaps, and prevent downstream execution when validation fails.
 * Create ingestion and normalization systems that can handle messy exports, historical records, customer-provided files, and inconsistent provider formats.
 * Develop evals, testing harnesses, observability, and feedback loops to measure AI quality in workflows where "almost always right" is not good enough.
 * Partner closely with product, design, operations, compliance, and platform teams to identify high-impact AI opportunities and turn them into reliable product capabilities.
 * Work through ambiguous product areas, prototype quickly, and scale successful solutions into reusable product and platform primitives.
 * Help set the engineering bar for correctness, auditability, security, reliability, and production safety in AI-enabled and new AI native financial products.