AI/ML Evaluation Engineer - Global Solutions Provider (Mexico)
Company:Truelogic
Location:Mexico
Languages:English
Skills:
PythonSQLArtificial IntelligenceData AnalysisMachine LearningNumpyPandasCI/CD
Requirements:
- Advanced Python skills, including writing, debugging, and automating scripts.
- Strong SQL proficiency and experience manipulating large datasets.
- Hands-on experience with Python libraries such as Pandas and NumPy.
- Ability to clean, standardize, and analyze structured and unstructured data.
- Experience inspecting datasets, visualizing distributions, and preparing data for analysis.
- Solid understanding of large language models, prompt behavior, hallucinations, and grounding concepts.
- Knowledge of retrieval-augmented generation (RAG) flows and embedding-based search.
- Awareness of vector similarity concepts such as cosine similarity and dot product.
- Experience with at least one LLM evaluation framework (RAGAS, TruLens, LangSmith, etc.) or ability to quickly learn one.
- Ability to design or implement custom LLM-as-Judge evaluation systems.
- Applied understanding of statistical concepts such as variance, confidence intervals, precision/recall, and correlation.
- Ability to translate ambiguous quality expectations into measurable metrics.
- Familiarity with cloud-run services and automation pipelines, preferably on Google Cloud Platform (GCP).
- Ability to learn new infrastructure tools quickly.
- Strong analytical and problem-solving abilities for open-ended technical challenges.
- Excellent communication skills for collaborating with cross-functional teams and presenting technical findings.
Responsibilities:
- Write Python and SQL scripts to evaluate LLM outputs.
- Design and implement LLM-as-Judge evaluations with scoring rubrics.
- Define and calculate metrics like exact match, token-level F1, ROUGE, cosine similarity, and rubric scores.
- Build and maintain ground-truth datasets for benchmarking and regression testing.
- Automate evaluation workflows and integrate them into CI/CD pipelines.
- Analyze large unstructured datasets to identify inconsistencies, anomalies, biases, and missing values.
- Diagnose failure modes such as hallucinations and irrelevant answers.
- Produce clear reports summarizing evaluation findings and quality trends.
- Collaborate with AI engineers, QA, data scientists, and product managers.
- Document all processes, evaluation setups, specifications, and architecture diagrams.
- Maintain reproducibility and traceability for all evaluation runs and datasets.
