Lead AI Engineer
Mexico - Mexico City
MXN 1040K-1462K (estimate) Senior-level Full Time
Tasks
- Build agent flywheel feedback loops
- Build offline and online evaluation frameworks
- Collect and structure agent traces into training datasets
- Contribute to model serving evaluation and experimentation infrastructure
- Create evaluation datasets golden traces and regression test sets
- Define and monitor quality containment revenue impact latency metrics
- Deploy ML models in agent workflows
- Design hybrid systems combining deterministic logic model scoring and LLM generation
- Design scalable data pipelines for batch and near realtime
- Develop production grade ML models for business applications
- Develop scalable Python services and APIs for agent workflows
- Ensure data quality, consistency, and availability
- Evaluate agents using LLM and human in the loop signals
- Implement continuous retraining and evaluation pipelines
- Implement reusable agent patterns for application workflows
- Integrate AI agents with tool and API usage
- Monitor reliability observability and performance of deployed systems
- Optimize prompts policies and model selection
- Run A B experiments to measure business impact
- Track outcomes from production signals
- Transform interaction data into features labels and evaluation datasets
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | API Development | Apache Airflow | Apache Spark | B testing | Backend Services | Causal Inference | Classification | Dagster | Data Lineage | Data Pipelines | Data Quality | Data Warehousing | Data orchestration | ELT | ETL | Experiment design | Forecasting | Google BigQuery | LLM orchestration | Language Models | Language Processing | Large Language Models | Machine Learning | Model Monitoring | Model Serving | Natural Language | Natural Language Processing | Observability | Python | Ranking | Recommendation Systems | Regression | Reinforcement Learning | Snowflake | Supervised Learning
Education
N/A
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