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AI Evaluation / Testing (Evaluation Engineer)
Fort Worth, TX (Remote)
Long Term Contract
Job Summary:
We are seeking a skilled AI Evaluation Engineer to validate AI models and agent workflows built on AWS and Azure as the core AI foundation, with Microsoft Copilot as the primary user experience layer. The role is responsible for ensuring AI systems meet rigorous standards for accuracy, safety, bias, and performance through structured testing, benchmarking, and continuous evaluation pipelines across the full AI lifecycle. The candidate will work closely with AI Architects, AI Engineers, and AI Security Engineers to establish evaluation frameworks that provide confidence in AI outputs before and after production deployment, including Copilot-integrated workflows and RAG-based systems.
Key Responsibilities:
Evaluation Framework Design
Design, build, and maintain end-to-end AI evaluation frameworks covering accuracy, relevance, groundedness, safety, fairness, and performance for LLM-powered systems on AWS and Azure.
Define evaluation strategies tailored to specific AI use cases including RAG pipelines, multi-agent workflows, and Microsoft Copilot-integrated experiences.
Establish standardised scoring rubrics, evaluation metrics, and acceptance thresholds in collaboration with AI Architects and business stakeholders.
Build reusable evaluation datasets, test suites, and golden-set benchmarks representative of real enterprise use cases and edge conditions.
Model & Agent Testing:
Execute structured testing of LLM models, RAG pipelines, and agentic workflows on AWS Bedrock and Azure AI Foundry to validate outputs against defined quality standards.
Test multi-agent orchestration logic including routing, handoff behaviour, context retention, tool use, and escalation pathways under a range of real-world scenarios.
Validate prompt engineering changes, model updates, and retrieval strategy modifications through systematic regression and A/B testing pipelines.
Conduct adversarial testing including prompt injection, jailbreak attempts, and boundary condition probing to assess model robustness and guardrail effectiveness.
Test Microsoft Copilot-integrated workflows and plugins for accuracy, response quality, and alignment with enterprise governance policies.
RAG & Retrieval Evaluation:
Evaluate RAG pipeline quality across the full retrieval chain, including chunking strategies, embedding quality, vector search relevance, re-ranking accuracy, and context utilisation.
Measure retrieval performance using precision, recall, mean reciprocal rank (MRR), and normalised discounted cumulative gain (nDCG) metrics across Azure AI Search and Amazon OpenSearch.
Assess grounding quality and citation accuracy of LLM responses to ensure outputs are faithfully anchored to retrieved enterprise data.
Identify and report retrieval gaps, knowledge staleness, and context window inefficiencies, and work with AI Engineers to drive improvements.
Safety, Bias & Responsible AI Testing:
Design and execute safety evaluation suites to detect harmful, toxic, or policy-violating AI outputs across production and pre-production environments.
Conduct bias and fairness assessments across demographic groups and use case domains to identify discriminatory patterns in AI model outputs.
Validate the effectiveness of guardrails, content filters, and refusal behaviours implemented by AI Security Engineers across AWS Bedrock and Azure AI Foundry safety layers.
Produce responsible AI evaluation reports that evidence compliance with enterprise AI governance standards, regulatory requirements, and Microsoft Responsible AI principles.
Continuous Evaluation Pipelines:
Build and maintain automated, continuous evaluation pipelines integrated into CI/CD workflows to catch quality regressions before deployment to production.
Implement production monitoring to detect model drift, output degradation, hallucination rate increases, and latency regressions in live AI systems on AWS and Azure.
Define alerting thresholds and feedback loops that trigger re-evaluation or rollback when AI system quality falls below agreed acceptance criteria.
Maintain evaluation run history, benchmark versioning, and quality trend dashboards to provide visibility of AI system health over time.
Benchmarking & Performance Testing:
Conduct performance benchmarking of AI models and inference endpoints on AWS and Azure, measuring latency, throughput, token efficiency, and cost-per-query under realistic load conditions.
Compare model versions, retrieval strategies, and prompt configurations against baseline benchmarks to quantify the impact of changes before production promotion.
Benchmark Microsoft Copilot-integrated workflows for end-to-end response time, accuracy, and user experience quality across enterprise use cases.
Produce clear benchmarking reports that support evidence-based decisions on model selection, infrastructure sizing, and optimisation priorities.
Collaboration & Quality Advocacy:
Partner with AI Engineers and AI Architects to integrate evaluation gates into the AI development and deployment lifecycle from design through to production.
Work with AI Security Engineers to align adversarial testing and safety evaluation activities with the broader AI risk and compliance framework.
Communicate evaluation findings clearly to technical and non-technical stakeholders, providing actionable recommendations and quality sign-off for AI releases.
Champion a culture of AI quality and continuous improvement across the delivery team, contributing to shared evaluation standards and best practices.
Required Qualifications:
6-10 years of experience in software testing, data science, or AI/ML engineering with 3+ years focused on evaluation, testing, or quality assurance of LLM-powered or AI systems in production.
Hands-on experience evaluating AI workloads on AWS (Bedrock, SageMaker) and Azure (Azure AI Foundry, Azure ML) including model testing, RAG evaluation, and agent workflow validation.
Experience testing Microsoft Copilot-integrated solutions, Copilot plugins, or Microsoft 365 AI features for quality, accuracy, and governance compliance.
Strong understanding of LLM evaluation metrics including BLEU, ROUGE, BERTScore, faithfulness, relevance, coherence, and task-specific scoring methodologies.
Experience with RAG evaluation frameworks and retrieval metrics (MRR, nDCG, precision, recall) across vector search platforms such as Azure AI Search and Amazon OpenSearch.
Familiarity with responsible AI evaluation principles including bias detection, fairness assessment, safety testing, and regulatory compliance validation.
Experience building automated evaluation and CI/CD pipelines; proficiency in Python and familiarity with evaluation frameworks such as Azure AI Evaluation SDK, Ragas, or DeepEval.
Strong analytical and communication skills with the ability to translate complex evaluation findings into clear quality assessments and release recommendations.