AI Evaluation Engineer

Telecommuter FL

R80152

By joining Sedgwick, you'll be part of something truly meaningful. It’s what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there’s no limit to what you can achieve.

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The AI Evaluation Engineer designs, builds, and operates the evaluation systems that determine whether our GenAI solutions are accurate, safe, reliable, and ready for production. You will serve multiple product pods, establishing consistent evaluation standards, automated pipelines, and quality gates, and you will help teams measure and improve LLM, RAG, and agentic applications throughout their lifecycle.

Fully remote role, open to U.S. based candidates.

• Establishes standardized, automated evaluation frameworks for LLMs and traditional ML models, including LLM as a judge patterns, ROUGE, METEOR, and statistical validation to ensure performance, reliability, and readiness prior to production release.

• Deploys comprehensive observability solutions to monitor model drift, hallucination risk, latency, throughput, and token consumption, balancing model quality with infrastructure cost and operational efficiency.

• Define evaluation methodologies and metrics for LLM, RAG, and agent-based applications (e.g., faithfulness, groundedness, relevance, task completion, tool-use correctness, safety).

• Build reusable evaluation frameworks, templates, and libraries that pods can adopt with minimal effort.

• Partner with product owners and SMEs to translate business requirements into measurable quality criteria and acceptance thresholds.

• Develop automated evaluation pipelines integrated into CI/CD, with regression testing and quality gates for prompt, model, and retrieval changes.

• Implement offline evaluation (benchmark and golden datasets) and online evaluation (production monitoring, sampling, drift and quality alerts).

• Support A/B and shadow testing for model, prompt, and architecture changes.

• Design, validate, and maintain LLM-as-a-judge evaluators, including rubric design, bias mitigation (position, verbosity, self-preference), and calibration against human labels.

• Establish human annotation workflows, labeling guidelines, and inter-annotator agreement practices with SMEs.

• Track judge reliability over time and re-calibrate as models and use cases change.

• Build and curate golden datasets, adversarial and edge-case suites, and synthetic test data generation approaches.

• Apply approved data-handling practices (de-identification, synthetic data, access controls) for any dataset derived from sensitive or regulated data.

• Implement red-teaming, hallucination, toxicity, PII leakage, prompt-injection, and jailbreak testing.

• Produce evaluation evidence and reporting that supports security, privacy, compliance, and model risk reviews.

• Contribute to responsible AI standards and release-readiness criteria.

• Embed with pods to onboard them to evaluation practices, then coach teams toward ownership of their own eval suites.

• Build dashboards and reporting that give engineering and business leaders transparent quality and cost/latency trends.

• Evaluate and recommend eval tooling and platforms; document standards and best practices.

• Performs other duties as assigned.

• Travel as required.

Master’s degree in a computer science or a quantitative discipline from an accredited college or…