[Hiring] Senior AI Systems Quality Engineer @Abacus Insights

Role Description

At Abacus Insights, we’re building AI‑powered platforms that sit at the center of critical healthcare decisions—where reliability, correctness, and trust matter as much as innovation. The Senior AI Systems Quality Engineer plays a critical role in making our AI systems production‑ready, operable, and trustworthy.

• This is not a traditional QA role; you’ll help define how quality is engineered into agentic systems from the very beginning.
• Embed guardrails, evaluation signals, and safe‑failure behavior directly into how these systems are designed, built, and deployed.
• Help design and own an AI testing platform that operates natively within our Databricks Medallion architecture.
• Leverage tools like MLflow to enable evaluation, lineage, traceability, and auditability at scale.
• Focus on establishing repeatable, scalable quality practices rather than point-in-time validation.
• Partner closely with AI Engineers, platform teams, and delivery stakeholders to design custom evaluation frameworks, validation pipelines, and automated testing harnesses.
• Validate orchestration logic, manage non‑deterministic behavior, and ensure AI systems degrade safely under uncertainty, scale, and failure.
• Build end-to-end automated AI testing integrated into the development lifecycle.
• Shape how reliable, governed, and scalable agentic AI is built from the ground up.

Qualifications

• 7+ years of software engineering experience, primarily in backend or platform systems.
• Proven experience designing and implementing AI testing automation in production environments.
• Demonstrated ability to build custom validation, evaluation, or testing frameworks for complex, distributed systems.
• Strong proficiency in Python and/or TypeScript within modern AI engineering stacks.
• Hands-on experience with AI-powered systems, including LLM-based or agentic workflows.
• Experience designing or contributing to AI testing at scale, including regression frameworks and large test coverage.
• Deep understanding of CI/CD integration, including embedding automated tests and quality gates into deployment pipelines.
• Solid understanding of AWS cloud-native architectures.
• 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.
• Experience with AI testing methodologies, including evaluation of non-deterministic outputs and robust regression strategies.
• Proven ability to establish measurable trust thresholds for AI systems.
• Experience working with domain experts to define correctness and real-world validation scenarios.

Requirements

• 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.
• Experience designing and iterating on prompts, agent behaviors, and orchestration logic.
• Familiarity with using AI systems to generate and expand test scenarios.

Benefits

• Unlimited paid time off – recharge when you need it.
• Work from anywhere – flexibility to fit your life.
• Comprehensive health coverage – multiple plan options to choose from.
• Equity for every employee – share in our success.
• Growth-focused environment – your development matters here.
• Home office setup allowance – one-time support to get you started.
• Monthly cell phone allowance – stay connected with ease.