About RiskExec

RiskExec is a rapidly growing SaaS company that delivers a best-in-class compliance analytics and reporting platform to help financial institutions and lenders comply with key government regulations and unlock new growth opportunities.

Working at RiskExec

RiskExec builds compliance analytics and reporting software that helps financial institutions meet regulatory requirements (fair lending, HMDA/CRA/1071) with less risk, measurable reduction in human review load, and structured, explainable AI-generated intelligence.

This role exists now because we’re accelerating two things at once:

1. Transforming and scaling a production-grade compliance platform into modular, resilient infrastructure for regulated institutions, and
2. Operating production AI systems under regulatory constraints that materially change how compliance work gets done—faster, more automated, and more defensible.

Leveling: This is a Senior role—typically 7+ years, based on scope and impact.

Location: Knoxville , TN (hybrid), Washington, DC (hybrid), Chicago, IL (hybrid), or United States (remote).

Responsibilities

1. What You Will Own

• End-to-end delivery of customer-facing product work: from design/estimation through implementation, testing, release, and production support.
• Production LLM system design and deployment inside our platform (not demos): Retrieval-augmented reasoning pipelines, Agentic workflow automation with human-in-the-loop, and AI-orchestrated workflow acceleration with measurable ROI and error.
• Production reliability under deterministic + probabilistic system: performance, reliability, security, and auditability—especially where AI is involved.
• API and data-layer correctness: clear contracts, sane schemas, predictable behavior, and defensible outputs.
• Formal AI evaluation, validation, and governance framework: Prompt lifecycle management and versioned model governance, Offline and online model evaluation pipelines, Behavioral regression testing for model outputs, and Quantitative evaluation benchmarks.
• Architectural decomposition and service boundary enforcement: improve or replace legacy ASP.NET/jQuery areas when it unlocks speed, quality, and maintainability.
• Engineering hygiene: readable code, pragmatic abstractions, tests that matter, and documentation that makes future changes faster.

2. How You Will Drive Impact

• You’ll operate like an owner: pick up ambiguous problems, clarify “what good looks like,” and ship in tight increments.
• You’ll use AI coding agents and development tooling(e.g., Copilot/Claude Code-style assistants) to move faster—while maintaining standards for correctness, security, and clarity.
• You’ll treat AI systems as governed infrastructure with risk: define acceptance criteria, failure modes, and safe fallbacks.
• You’ll ship with an execution cadence that matches a PE-backed operating model:
• • small releases, frequent integration
  • measurable outcomes (adoption, time saved, defect rate, latency)
  • tight feedback loops with customers and internal stakeholders
• Typical leverage points you’ll influence:
• • API performance and stability
  • reducing manual compliance workflows via automation
  • Distributed + AI observability and traceability of core services
  • making Schema-bound, verifiable model outputs (lower Model uncertainty and output validation risk, better traceability)

Current stack (context, not dogma): .NET 8 / C#, TypeScript, SQL Server, containerized services, Azure; plus legacy ASP.NET/jQuery in parts of the product.

3. Cross-Functional & Executive Interfaces

• Product & Customer teams: translate workflow pain into shippable increments; validate impact quickly.
• Compliance/Domain experts: align on defensibility, audit expectations, and “what must never be wrong.”
• Engineering leadership: architecture decisions, reliability targets, and prioritization tradeoffs.
• Natural tension you’ll manage: speed vs. correctness, AI capability vs. explainability, modernization vs. delivery.

Qualifications

Required

• Typically 7+ years shipping production software with real users, real data and real consequences.
• Strong backend engineering skills in C#/.NET (or equivalent),  including API design, data modeling, performance tuning, and concurrency.
• Experience designing and operating distributed services with explicit contracts, versioning discipline, and backward compatibility.
• Deep SQL fluency (schema design, indexing strategy, query optimization); experience with SQL Server or Postgres.
• Proven ownership of a problem domain: architecture decisions, delivery, production reliability, and ongoing evolution.
• Experience integrating external APIs or model providers into production systems with clear failure handling and fallback logic.
• Ability to design systems that combine deterministic code and probabilistic model outputs safely (validation layers, schema enforcement, guardrails).
• Experience defining measurable acceptance criteria for system behavior (including AI outputs where applicable).
• Comfort working in containerized/cloud environments (Azure/AWS/GCP) and understanding operational tradeoffs.
• Practical use of AI coding agents or copilots to accelerate development without compromising correctness, security, or maintainability.
• Strong debugging and production incident response skills — calm under pressure, bias toward root cause.

Preferred

• Shipped production LLM systems (not prototypes), including RAG pipelines, embeddings/vector search, tool/function calling, structured generation, or multi-step agentic workflows.
• Experience tuning retrieval systems (chunking strategy, embedding selection, relevance scoring, hybrid search).
• Designed evaluation frameworks for model behavior (offline eval datasets, regression testing, human-in-the-loop review, measurable quality thresholds).
• Experience implementing schema-constrained or strongly typed model outputs with validation and rejection handling.
• AI observability: prompt tracing, response logging, drift detection, latency/cost monitoring, and output explainability.
• Experience managing model lifecycle decisions (model selection, fallback strategies, cost-performance tradeoffs).
• Experience building systems under regulatory, compliance, or audit scrutiny.
• Azure-native infrastructure depth (App Services, Functions, AKS, storage patterns) and containerized deployments (Docker/Kubernetes).
• CI/CD maturity, infrastructure-as-code awareness, and production monitoring discipline.
• Strong TypeScript front-end architecture with a modern framework (React/Vue/Svelte) and component-level design rigor.
• End-to-end testing strategy including Playwright/Selenium and pragmatic test pyramid thinking.
• Experience optimizing inference latency and cost at scale.

Traits that Win at RiskExec

• You move fast and you’re rigorous: “quick” never means sloppy.
• You think in systems: you see second-order effects and prevent recurring problems.
• You communicate clearly: crisp proposals, high-signal PRs, honest tradeoffs.
• You’re steady in production and proactive about reliability.
• You’re motivated by fairness and compliance outcomes because they define “correct” here.
• You use AI as a force multiplier while maintaining engineering ownership—and you can explain behavior when it matters.

If you do this job well, banks and lenders spend less time wrestling with compliance reporting and more time making better, fairer decisions—with outputs they can defend. You’ll help build the next generation of compliance software: more automated, more intelligent, and meaningfully faster—without compromising trust.