Senior AI Evaluation & Reliability Engineer

Why Aubergine

What You Will Own

1. Make AI Performance & ROI Measurable
   1. Build evaluation strategies that connect AI performance to business outcomes and ROI.
   2. Define quality benchmarks, SLOs, risk thresholds, and success criteria.
   3. Track hallucinations, reliability issues, and production risks.
   4. Build executive-friendly AI quality and ROI scorecards.
   5. Help clients determine where AI should be autonomous, supervised, or avoided.
   6. Don't just measure model accuracy. Measure business impact.
2. Build Production-Grade Evaluation Pipelines
   1. Architect automated evaluation pipelines for LLMs, RAG, and agentic systems.
   2. Integrate evaluations into CI/CD using GitHub Actions, GitLab CI, or equivalent.
   3. Build regression suites for prompts, models, tools, and workflows.
   4. Measure faithfulness, context precision, answer relevance, semantic drift, and task completion.
   5. Establish statistically meaningful benchmarks and continuously monitor AI quality.
   6. Evaluation should become part of engineering, not a final QA step.
3. Engineer LLM-as-a-Judge Systems
   1. Design reference-based and reference-free LLM evaluation frameworks.
   2. Create structured rubrics and scoring systems.
   3. Build calibration loops using human-labelled datasets.
   4. Identify and mitigate judge biases such as position, verbosity, and self-preference bias.
   5. Measure judge reliability and optimize evaluation quality, latency, and cost.
4. Own Evaluation Economics
   1. LLM evaluations can become expensive quickly.
   2. You will:
   3. Design tiered evaluation strategies.
   4. Use deterministic and heuristic graders for simple checks.
   5. Reserve powerful LLM judges for complex evaluations.
   6. Optimise batching, concurrency, and parallel execution.
   7. Track evaluation cost and balance quality, latency, and inference spend.
5. Benchmark RAG & Agentic Systems
   1. Build systematic benchmarks across:
   2. Retrieval quality, faithfulness, and answer relevance
   3. Embedding, chunking and re-ranking strategies
   4. Vector databases and retrieval architectures
   5. Agent task completion and tool-calling accuracy
   6. State retention, multi-step workflows and failure recovery
   7. Latency, reliability and cost
   8. Build synthetic datasets, golden test suites, and adversarial scenarios to continuously expand evaluation coverage.

Be a Trusted AI Consultant

• You will work directly with North American enterprise clients as a technical AI advisor.
• You will:
• Lead AI architecture and evaluation discussions.
• Translate complex AI metrics into clear business recommendations.
• Define AI quality, reliability, and risk frameworks.
• Present evaluation telemetry and ROI scorecards to technical and business stakeholders.
• Challenge assumptions and recommend the right AI solution, even when that means saying "don't use AI here."
• You should be equally comfortable discussing LLM evaluation with engineers and ROI with a CTO/COO/CEO/CFO.

Drive AI Consulting & Presales

• Participate in discovery calls, solution workshops, and technical discussions with prospective clients.
• Understand client challenges and identify where AI, agents, RAG, or automation can create meaningful business value.
• Shape AI solution approaches, evaluation strategies, and technical proposals.
• Contribute to statements of work, estimates, solution architectures, and project proposals.
• Build compelling technical narratives and demonstrations that showcase our AI capabilities.
• Present AI solutions and recommendations to technical and executive stakeholders.
• Help identify opportunities to expand existing engagements through new AI capabilities.
• Partner closely with Sales, Delivery, Product, and Engineering teams to convert opportunities into successful engagements.
• Bring a consultative, outcome-focused approach rather than simply responding to client requirements.

Coach Engineers. Raise the Bar.

• Mentor engineers working on AI and LLM systems.
• Establish AI engineering and evaluation best practices.
• Conduct technical workshops and knowledge-sharing sessions.
• Review AI architectures and evaluation strategies.
• Help engineers move from prompt experimentation to disciplined AI engineering.
• Build reusable frameworks, playbooks, and internal accelerators.
• Stay ahead of emerging AI technologies and bring valuable ideas into the organisation.

What We Are Looking For

AI Evaluation & Reliability

• Proven experience building production-grade AI evaluation systems.
• Strong practical experience with LLM-as-a-Judge.
• Experience with calibration, human ground truth, and evaluation bias.
• Strong understanding of RAG and agent evaluation.
• Knowledge of Faithfulness, Context Precision, Answer Relevance, Task Completion, and semantic similarity.
• Experience with DeepEval, Ragas, TruLens, LangSmith, DSPy, Promptfoo, or equivalent.

Engineering

• Advanced Python and/or TypeScript.
• Strong backend, API, and asynchronous systems experience.
• Experience with CI/CD and automated testing.
• Experience with vector databases such as Pinecone, Qdrant, Milvus, or Chroma.
• Understanding of LLM infrastructure, observability, and inference economics.

Consulting & Leadership

• Experience working directly with enterprise clients.
• Excellent communication and presentation skills.
• Strong consulting and problem-solving mindset.
• Ability to translate technical complexity into business outcomes.
• Strong mentoring and coaching abilities.
• Curiosity, ownership, and enthusiasm for emerging AI technologies.

Why This Role Matters