QA AI Automation Engineer

Dynasty Financial Partners, LLC•St. Petersburg, FL

About The Position

We are building AI systems that advisors and their clients rely on inside a regulated wealth management platform — an internal AI assistant, an external-facing API layer on top of it, and a growing set of agentic workflows. The bar for correctness, grounding, and auditability is higher here than it is for most AI products. This role exists because traditional QA does not cover that surface. We need an engineer who can evaluate non-deterministic systems, build the tooling that catches AI regressions before they reach an advisor, and hold the line on code quality now that a meaningful share of our code is written by autonomous coding agents. This is a hands-on engineering role, not a test-execution role.

Requirements

5+ years in software quality, test automation, or SDET work, with real ownership of automation architecture — not just test authoring.
Demonstrated production experience with LLM-powered systems: agent patterns, prompt engineering, tool/function calling, and orchestration.
Hands-on with major model APIs (OpenAI, Anthropic, Azure OpenAI) and AI-assisted development tooling (Claude Code, Copilot, Codex or equivalent).
Strong programming in at least one of Python, TypeScript/JavaScript, Java, Kotlin, or C#, and comfort reading across the others.
Experience building and interpreting evaluation criteria for systems without a single correct answer.
Deep CI/CD fluency — pipeline integration, gating, monitoring, and logging.
API testing depth and experience validating third-party integrations.
Clear written communication; you can explain a quality risk to a product owner and a root cause to an engineer in the same day.

Nice To Haves

Eval and observability tooling: Langfuse, Promptfoo, OpenTelemetry, New Relic, or equivalents.
RAG fundamentals — embeddings, chunking strategy, vector search, retrieval evaluation.
Workflow orchestration tooling (n8n or similar).
Azure cloud services; .NET ecosystem exposure.
BDD/Selenium or comparable UI automation at scale.
Financial services, wealth management, or another regulated domain — or a clear appetite for what data governance means in one.
Experience mentoring or leading distributed QA engineers.

Responsibilities

Own and extend our AI evaluation framework for AI chat, including building and maintaining an evaluation suite for our AI assistant and its API surface, covering correctness, grounding/citation fidelity, retrieval quality, refusal and escalation behavior, tone, latency, and cost per interaction.
Curate and version golden datasets and adversarial test sets drawn from real advisor workflows, keeping them representative as the product changes.
Design LLM-as-judge and rubric-based scoring where deterministic assertions do not apply and validate the judges themselves against human-labeled sets.
Integrate evaluations into CI so prompt, model, retrieval, and tool changes are gated by measured regression.
Instrument production traces and close the loop from live failures back into the eval suite.
Build the multi-agent AI quality step that finds defects automatically, including designing and operating a multi-agent workflow in our CI/CD pipeline that executes the suite, triages failures, writes up defects with reproduction detail, proposes or applies fixes, and re-runs to verify.
Define agent roles, hand-off logic, guardrails, and the quality checks that decide when an agent's output is trustworthy enough to auto-apply versus route to a human.
Make cost-aware calls on where an LLM is needed and where deterministic automation is the better tool.
Report on the loop's real impact, including autonomous resolution rate, false-positive rate, and engineer hours returned.
Scale code quality in the age of autonomous agent coding by defining what “good” means for agent-authored code and building automated gates that enforce it, such as coverage and mutation testing, static analysis, security and dependency scanning, architectural conformance, and review checklists tuned to how agents fail.
Identify failure modes specific to agent-generated code (e.g., plausible-but-wrong logic, silent scope creep, duplicated abstractions, missing edge-case handling, tests written to pass rather than to verify) and build detection for them.
Partner with architects and delivery teams to keep velocity from outrunning quality as agent-assisted development scales across the organization.
Partner with product, engineering, and AI Labs to define coverage and acceptance criteria for AI features before they are built.
Contribute to release-readiness and go/no-go decisions with evidence.
Maintain and modernize our existing automation estate (API, UI, integration) and mentor engineers on its use.