Senior AI Systems Quality Engineer

Join Abacus Insights, a company dedicated to improving healthcare through AI-powered platforms. As a Senior AI Systems Quality Engineer, you will play a critical role in ensuring the reliability, correctness, and trustworthiness of our AI systems. This is not a traditional QA role; you will help define how quality is engineered into agentic systems from the very beginning. You will design and own an AI testing platform, establish repeatable quality practices, and partner closely with AI engineers and delivery stakeholders. Your work will directly influence release readiness, operational confidence, and Abacus' ability to responsibly deploy advanced AI in mission-critical healthcare environments.

- Concevoir et mettre en œuvre des cadres de validation, d'évaluation ou de test personnalisés pour des systèmes complexes et distribués. - Collaborer avec les équipes d'ingénierie AI, les équipes de plateforme et les parties prenantes pour concevoir des cadres d'évaluation personnalisés, des pipelines de validation et des dispositifs de test automatisés. - Établir des pratiques de qualité répétables et évolutives, en intégrant des tests automatisés et des portes de qualité dans les pipelines de déploiement.

- Experience working with domain experts to define correctness and real-world validation scenarios, enabling large-scale, business-relevant test coverage that reflects true production use cases rather than engineering-only perspectives - Demonstrated ability to build custom validation, evaluation, or testing frameworks for complex, distributed systems - Hands-on experience with AI-powered systems, including LLM-based or agentic workflows and non-deterministic behavior - Track record of engineering for quality, reliability, governance, and safety as core system design principles - Working knowledge of security, privacy, and operational risk in regulated or mission-critical environments, including failure modes and recovery - 7+ years of software engineering experience, primarily in backend or platform systems - Proven ability to establish measurable trust thresholds for AI systems, including defining and operationalizing success metrics such as query accuracy, hallucination limits, explainability, and PHI-safe behavior as enforceable release criteria - Strong proficiency in Python and/or TypeScript within modern AI engineering stacks - Proven experience designing and implementing AI testing automation in production environments, not just executing tests - Deep understanding of CI/CD integration, including embedding automated tests and quality gates into deployment pipelines - Experience designing or contributing to AI testing at scale, including regression frameworks, long-tail evaluation, and large test coverage - Solid understanding of AWS cloud-native architectures - Experience with AI testing methodologies, including evaluation of non-deterministic outputs, drift detection, bias/fairness testing, and robust regression strategies - Experience with Databricks-native environments and Medallion architecture - Experience using MLflow for model evaluation, lineage tracking, and auditability - Exposure to observability tools (e.g., Datadog, Prometheus, Grafana) for distributed AI workflows - Familiarity with LLM evaluation techniques, guardrails, and policy enforcement frameworks - Experience evaluating performance, latency, or cost regressions in AI systems - Ability to clearly document system behavior and quality trade-offs for technical and business audiences - Formal training or certification in AI/ML systems (e.g., ISTQB AI Testing, AWS ML Specialty, Google ML Engineer) - Experience designing and iterating on prompts, agent behaviors, and orchestration logic as versioned, testable artifacts, enabling rapid refinement of AI system behavior (often referred to as “vibe coding”) without heavy reliance on traditional code changes - Familiarity with using AI systems to generate and expand test scenarios, including creating large-scale, diverse, and adversarial test datasets to significantly improve coverage and validation depth