QA Automation Engineer

This is a remote position.

This is a remote position.

As QA Automation Engineer, you will build and evolve the quality-engineering and continuous-testing capability for the reView Decision Intelligence platform.

reView is an enterprise platform composed of distributed services, APIs, integrations, a graph knowledge layer, AI/model services, orchestration capabilities, and web-based experiences. It runs across AWS, containerized, and Kubernetes environments - and, for regulated and federal customers, in on-premises, disconnected, and air-gapped deployments. Quality engineering is what proves these capabilities function together securely, reliably, accurately, and at enterprise scale.

You will embed quality throughout the development lifecycle using shift-left testing, continuous testing, automated code assurance, CI/CD quality gates, and release-readiness evidence. You will work closely with Product, Architecture, Engineering, and Platform/DevSecOps to establish expected platform behavior and translate it into repeatable automated validation.

The Head of Product Engineering owns the quality standard. You own the automation capability that enforces it.

Your work will validate that:

•     reView platform features, services, APIs, and integrations function correctly across every supported deployment mode - cloud, containerized, on-premises, edge, and air-gapped.

•     Decision Records are complete, source-linked, reproducible, and defensible.

•     Reasoning remains bounded to the customer’s governed corpus.

•     The platform meets defined performance, scalability, reliability, and recovery requirements under enterprise workloads.

•     Security, software assurance, accessibility, integration, and regression requirements are continuously validated.

•     Releases carry objective evidence that defined quality and release-readiness requirements have been satisfied.

The automation capability you build will support both the Global Product and Federal delivery pipelines, creating a common quality foundation across data² product delivery.

This position requires work within U.S.-controlled development, testing, and federal software-delivery environments.

1. Requirements & Shift-Left Quality

Embed quality and testability early in the development lifecycle.

•     Partner with Product, Architecture, and Engineering to refine requirements, user stories, and acceptance criteria.

•     Translate platform requirements into measurable, automatable test conditions.

•     Define functional, integration, security, reliability, performance, and applicable accessibility acceptance criteria.

•     Identify testability, observability, API contract, and error-condition requirements early in development.

•     Apply BDD and structured test-design practices to critical platform capabilities and workflows.

Tools / Skills: Jira, BDD concepts, Cucumber, SpecFlow, boundary analysis, equivalence partitioning.

2. Platform, API & Knowledge-Layer Testing

Develop automated validation across reView platform capabilities and distributed services.

•     Design and maintain automated tests for FastAPI services, APIs, microservices, integrations, and critical workflows.

•     Validate API contracts, authentication, authorization, error handling, asynchronous behavior, retries, timeouts, and recovery.

•     Validate the ingestion and knowledge pipeline end to end (reDesign → reEnrich → reMap → reVeal → reFocus), including graph construction, entity resolution, relationship accuracy, and traversal correctness.

•     Test positive, negative, boundary, exception, and failure conditions.

•     Validate technical integrations with enterprise systems, identity services, and external services.

•     Build regression coverage and reusable fixtures, mocks, and test data.

Tools / Skills: Python, pytest, FastAPI, Memgraph, Docker, Docker Compose, Kubernetes, AWS.

3. AI, Reasoning & Decision Record Validation

Validate the properties that make reView defensible - the hardest and most differentiated testing problem in this role.

•     Build evaluation harnesses for non-deterministic model output: retrieval precision and recall, answer accuracy, and grounding.

•     Validate that reasoning stays bounded to the customer’s governed corpus and does not draw on model parametric knowledge.

•     Validate Decision Record completeness, citation fidelity, source traceability, and reproducibility.

•     Detect and regression-test hallucination, citation drift, and unsupported inference.

•     Establish regression validation across model swaps, prompt changes, and retrieval-configuration changes, so a model upgrade cannot silently degrade explainability.

•     Validate model-agnostic operation across supported LLMs and deployment modes, including locally hosted models in disconnected environments.

Tools / Skills: Python, pytest, eval frameworks, retrieval and grounding metrics, prompt and model regression tooling.

4. Automation Framework & Continuous Testing

Build and maintain the reusable automation capability supporting reView.

•     Develop automated API, service, integration, functional, end-to-end, performance, reliability, security, and accessibility testing.

•     Integrate automated testing throughout development and CI/CD pipelines.

•     Establish automated quality gates and fast developer feedback.

•     Support reliable execution across local, containerized, AWS, on-premises, and CI/CD environments - including validation paths for disconnected and air-gapped installations where pipeline egress is unavailable.

•     Maintain high-value regression suites while improving execution speed, stability, diagnostics, and coverage.

•     Generate automated quality evidence supporting Global Product and Federal releases.

Tools / Skills: Python, pytest, Docker, Kubernetes, AWS, Git-based version control, CI/CD pipelines.

5. Security & Software Assurance

Incorporate automated software assurance into the quality framework.

•     Support static analysis, code-quality checks, secrets scanning, dependency vulnerability analysis, SBOM generation, and malicious-code scanning.

•     Validate applicable container and infrastructure configuration controls.

•     Work with Platform/DevSecOps and Engineering to establish automated quality and security gates.

•     Ensure findings are traceable to applicable builds and releases.

•     Produce automated security and software-assurance evidence that supports release readiness and maps to SOC 2 change-management controls and, where applicable, NIST 800-171 / CMMC requirements - so evidence is generated once and used everywhere.

Tools / Skills: CI/CD security and code-assurance tooling, SBOM tooling, Docker, Kubernetes, AWS.

6. Performance, Scalability, Reliability & Accessibility

Validate that reView meets defined enterprise and federal operational-quality requirements.

•     Establish performance baselines and validate response time, throughput, concurrency, resource utilization, and scaling behavior.

•     Execute load, stress, scalability, and reliability testing under representative workloads, including graph query and retrieval load.

•     Validate AWS, Kubernetes, and on-premises cluster behavior under load, failure, resource constraints, and recovery conditions.

•     Identify performance and reliability regressions and incorporate applicable tests into continuous validation.

•     Incorporate accessibility testing for applicable platform experiences and deployment requirements.

•     Validate federal deployments against applicable Section 508 requirements (WCAG 2.1 AA) and support required accessibility evidence.

Tools / Skills: Standard load-testing tooling, AWS, Docker, Kubernetes, Grafana, Loki, accessibility testing tools, Section 508 / WCAG 2.1 AA.

7. Defect Analysis & Release Readiness

Provide objective quality evidence supporting platform and release decisions.

•     Analyze failures across services, APIs, integrations, the knowledge layer, user experiences, infrastructure, and cloud environments.

•     Document defects with clear reproduction steps, logs, expected behavior, and observed behavior.

•     Collaborate with Engineering on root-cause analysis, remediation validation, and regression coverage.

•     Track automation coverage, defects, security findings, accessibility, performance, and reliability.

•     Define and report measurable quality and release-readiness criteria.

•     Continuously improve automation effectiveness, coverage, reliability, and quality feedback.

Tools / Skills: Grafana, Loki, Jira.

Requirements

•     Authorized to work in the United States and eligible to work within U.S.-controlled development, testing, and federal software-delivery environments, including access to export-controlled technical data.

•     Strong experience testing backend systems, APIs, microservices, distributed systems, or complex enterprise platforms.

•     Strong experience designing, implementing, and maintaining automated test frameworks.

•     Strong Python skills and experience with pytest or similar frameworks.

•     Experience designing automated integration and regression tests across multiple services.

•     Experience integrating automated testing and quality gates into CI/CD pipelines.

•     Experience testing applications and distributed services operating in AWS cloud environments.

•     Experience with Docker and containerized development or test environments.

•     Understanding of API contracts, authentication, authorization, error handling, asynchronous processing, and distributed-service behavior.

•     Experience with performance, scalability, reliability, and failure-condition testing.

•     Strong debugging and root-cause-analysis skills across multiple technical layers.

•     Understanding of modern DevOps, shift-left quality, continuous testing, and continuous-delivery practices.

•     Ability to translate product and technical requirements into objective automated validation.

•     Strong written and spoken English for collaboration across a distributed engineering organization.