AI Evaluation Engineer

Sedgwick; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Sedgwick is seeking an AI Evaluation Engineer for a fully remote role in the United States. You will design, build, and operate evaluation systems that ensure GenAI solutions are accurate, safe, reliable, and production-ready.

You will establish evaluation standards, automated pipelines, and quality gates across multiple pods, focusing on LLM, RAG, and agent-based applications, with collaboration from product and SME teams.

Qualifications

• Master's degree in a computer science or quantitative discipline.
• 4+ years in software, ML, or data engineering; 1–2 years with LLM-based apps.
• Proficient in Python and software engineering fundamentals (testing, VCS, CI/CD).
• Experience with evaluation metrics and statistical validation for ML models.
• Familiarity with eval/observability tools (LangSmith, Langfuse, MLflow) and cloud platforms.

Responsibilities

• Establish standardized evaluation frameworks for LLMs and ML models.
• Deploy observability solutions for drift, latency, and cost.
• Define metrics for LLM/RAG and agent-based apps.
• Build reusable eval templates and pipelines for CI/CD integration.
• Collaborate with product owners and SMEs to set quality criteria.

Skills

LLM evaluation

Python

CI/CD

Statistical validation

Observability

Data science collaboration

Cloud platforms

Education

Master's degree in computer science or quantitative discipline

Tools

LangSmith

Arize Phoenix

WhyLabs

Job description

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.

Newsweek Recognizes Sedgwick as America's Greatest Workplaces National Top CompaniesCertified as a Great Place to Work®Fortune Best Workplaces in Financial Services & Insurance

AI Evaluation Engineer

PRIMARY PURPOSE: 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.

ESSENTIAL FUNCTIONS AND RESPONSIBILITIES

• 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.

ADDITIONAL FUNCTIONS and RESPONSIBILITIES

• Performs other duties as assigned.
• Travel as required.

QUALIFICATIONS

Education & Licensing

Master's degree in a computer science or a quantitative discipline from an accredited college or university preferred.

Experience

4+ years in software, ML, or data engineering, with at least 1-2 years working hands-on with LLM-based applications (adjust to your market).

Skills & Knowledge

• Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs
• A strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance
• Strong Python skills and software engineering fundamentals (testing, version control, CI/CD, code review).
• Practical experience evaluating LLM/RAG systems, including retrieval metrics (recall, precision, MRR/nDCG) and generation metrics.
• Hands-on experience with LLM-as-a-judge design, prompt engineering, and judge validation.
• Working knowledge of statistics and experimental design: sampling, confidence intervals, significance testing, inter-rater agreement.
• Familiarity with at least one eval or observability tool (e.g., Ragas, DeepEval, promptfoo, LangSmith, Langfuse, MLflow, or equivalent).
• Experience with cloud platforms (Azure, AWS, or GCP) and their managed LLM services.
• Clear communication skills and the ability to work with engineers, product owners, SMEs, an