Note: The job is a remote job and is open to candidates in USA. Sedgwick is hiring an AI Evaluation Engineer to build and operate evaluation systems for its generative AI solutions. The role establishes evaluation standards, automated pipelines, quality gates, and monitoring to assess and improve LLM, RAG, and agent-based applications throughout their lifecycle.
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
- Performs other duties as assigned
- Travel as required
Skills
- 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)
- 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, and risk stakeholders
- Excellent oral and written communication skills, including presentation skills
- Ability to work in a team environment
- Ability to meet or exceed Performance Competencies
- Master's degree in a computer science or a quantitative discipline from an accredited college or university preferred
- Experience evaluating agentic systems (multi-step reasoning, tool calling, trajectory evaluation)
- Background in ML evaluation, search/relevance, NLP, or QA automation
- Experience with LLMOps, monitoring and observability, and production feedback loops
- Familiarity with AI risk and governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001) and secure development practices in regulated environments
- Experience with data annotation workflows and working with SMEs
- Knowledge of enterprise controls: SSO, RBAC, private networking, logging, and encryption
Benefits
- Fully remote role, open to U.S. based candidates.
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