Programmatic QA • Testing for LLMs & Agents • Data Quality • Platform Reliability

About Finalytics.ai

Finalytics.ai is the leading provider of personalization for the financial industry. Our platform combines data integrations, machine learning, and real-time technology to make digital experiences more relevant and higher-converting for credit unions and banks. We're a growing startup led by industry veterans, building the next generation of AI-driven personalization.

Why This Role Is Different

QA at Finalytics goes well beyond clicking through a UI. Our platform makes model-driven decisions, runs LLMs and agents that generate content and answer questions, and depends on data pipelines that feed those models every day — and all of it has to be tested programmatically.

We're looking for an engineering-minded QA team contributor to help build quality across three areas: our core personalization features, our LLM and agentic capabilities, and the data that powers them. This is a coding role, embedded in the same repo and release flow as our engineers that will report directly to the CTO. You won't just find bugs — you'll build the automated tests, evals, and data checks that let a small team ship trustworthy AI every sprint.

Our stack is Python/Django with a JavaScript personalization tag, backed by MySQL, Celery, BigQuery, and AWS.

What You'll Do

1. Programmatic QA of Core Features

Extend our scenario test runner — a proprietary harness that captures real production personalization requests and replays them across environments, asserting on expected algorithms and content selection. Grow it into automated regression across every client.
Write automated tests in Python with pytest across our tiers — unit, integration, HTTP, and end-to-end.
Build headless Playwright end-to-end tests to verify how personalized content and tracking render on real client pages.
Harden the pre-deploy quality gate and pre-commit checks that block bad changes automatically.

2. Testing & Standardizing LLMs and Agents

Design evals for non-deterministic AI features — our conversational analytics assistant, AI content builders, and generative SEO — measuring correctness, grounding, and regression across prompt and model versions.
Test the tool-calling and agentic layers — that function-calling loops pick the right tools and guardrails hold on adversarial input.
Validate our agent/MCP interface — contract conformance, rate limiting, authorization, and safe failure.
Help set our standards for shipping AI — catching hallucinations and drift, and benchmarking prompt/model changes before clients see them.

3. Data Quality Engineering

Build automated data-health checks that flag stale rollups, incomplete coverage, and broken aggregations before they hit a client dashboard.
Validate data pipelines end-to-end — rollups, funnel/rate/financial ingestion, and BigQuery — with drift detection across environments.
Guard model inputs so the signals our ML depends on stay accurate and complete.

4. Reliability & Performance

Track platform performance — response times, JS load, and page speed — and help keep it fast.
Stand up quality dashboards — uptime, coverage, data-health, and eval scores.

5. Collaboration & Bug Lifecycle

Work in the codebase alongside engineers to diagnose issues across development, release, and deployment.
Drive the bug lifecycle — reproduce, capture with a failing test, and verify the fix.

What We're Looking For

3+ years in QA/SDET or test automation with a code-first approach.
Strong Python — you write clean test code and can read the app you're testing.
pytest (preferred) and browser automation (Playwright or Selenium).
API and contract testing experience.
A genuine interest in testing AI — comfortable with non-determinism, evals, and prompts.
Data-savvy — strong SQL, and the instinct to validate pipelines and reconcile data.
Building automated quality gates into the deploy and release process.

Nice to Have

Testing or evaluating LLM applications — evals, prompt regression, tool-calling agents, or MCP.
Data or analytics QA — BigQuery or ETL/rollup validation.
Django, MySQL, or Celery experience.
Security testing with SAST/DAST tooling.
Familiarity with machine learning.
Financial industry, personalization, or CMS/marketing-platform experience.
Familiarity with AWS.
SaaS startup experience on a fast-moving, multi-tenant platform.

Why Finalytics

Frontier work — help define what QA means for AI, agents, and data-driven personalization in finance.
Direct impact — help shape how quality works across the platform, reporting straight to the CTO.
Automation-first culture — your work is code, in the same repo and release flow as engineering.
Remote-first, collaborative, low-ego team growing with a scaling fintech.

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