AI Evaluation & Reliability Engineer

abra R&D is looking for a Reliability Engineer!

abra R&D is looking for a Reliability Engineer who will take part in building the next-generation agentic analytics platform, the first real-time database optimized for AI agents at scale.

We’re looking for a Senior AI Evaluation & Reliability Engineer to define and build how AI agents are measured, validated, monitored, and improved in production. This role sits at the intersection of LLM systems, evaluation research, and production-grade engineering.

You will design evaluation methodologies, build LLM-as-a-judge systems, and develop agent-based testing frameworks to ensure correctness, robustness, and reliability of complex multi-agent workflows operating on real-time data.

What You’ll Do:

• Design and implement evaluation frameworks for AI agents and multi-agent systems
• Build LLM-as-a-judge pipelines to assess correctness, reasoning quality, and output quality
• Develop agent-based evaluation systems (agents evaluating agents) for scalable testing
• Define metrics, benchmarks, scorecards, and methodologies for agent reliability and performance
• Build data-driven evaluation pipelines using synthetic and real-world datasets
• Identify and analyze failure modes, edge cases, and non-deterministic behaviors
• Improve agent robustness, consistency, and reliability in production environments
• Work with tools such as Google ADK, Opik, and related evaluation frameworks
• Collaborate closely with AI, platform, and database teams to shape agent–data interaction quality

Requirements:

• 4–8+ years of experience in software engineering, AI systems, or evaluation/QA engineering
• Strong programming skills in Python
• Hands-on experience working with LLMs in production environments
• Experience building evaluation systems, automation frameworks, or testing infrastructure
• Strong understanding of prompt engineering, tool use, and agent behavior
• Ability to think in terms of metrics, correctness, and system reliability

Strong Advantage:

• Experience with LLM evaluation frameworks (Opik, LangSmith, etc.)
• Experience with Google ADK / agent frameworks
• Experience implementing LLM-as-a-judge or ranking systems
• Background in data systems, analytics, or real-time pipelines
• Experience with multi-agent systems
• Familiarity with statistical evaluation methods or experimentation (A/B testing, scoring systems)