Diego Pino

I act as technical lead for Generative AI initiatives at Iberia, focusing on the execution and evolution of the main customer-facing conversational platform deployed on Iberia’s public website.

The solution is a production-grade, multi-agent Generative AI system, handling 3M+ customer conversations per year, and integrated with core operational systems through MCPs.

Key responsibilities and contributions (Generative AI):

-Technical leadership of implementation teams, translating business decisions into production-ready solutions

-Active contribution to the design of the multi-agent GenAI architecture, including agent orchestration, shared memory, and API contracts

-Hands-on development of core agents (security, tone, translation, conversation management, flight cancellation)

-Design and maintenance of a shared Generative AI core library, reused across multiple initiatives

-LLMOps execution, including deployment pipelines, monitoring, evaluation, and observability

-Close collaboration with internal Iberia technical, product, and business stakeholders, contributing to technical decision-making and roadmap discussions

Additional responsibilities (Traditional ML & Customer Analytics):

-Technical supervision of 3 Data Scientists working on production ML models, including:

-Next Best Action

-Business vs Leisure segmentation

-Churn and purchase propensity models

-Recommendation systems

-Architectural and quality oversight of end-to-end ML pipelines, from data ingestion to deployment and monitoring

-Support in incident resolution, performance analysis, and continuous model improvement

Tech stack: Python, LLMs, Agent-based architectures, AWS (SageMaker, Fargate, DynamoDB, RDS, S3, Redis), Airflow, REST APIs, OpenTelemetry, Langfuse, CI/CD

I created and put into production end-to-end churn, propensity and recommendation models. I have designed a recommendation bot by combiningfoundational models (GPT-3.5, Llama2, Claude), LangChain architecture, and prompt engineering, which interacts with a database to report available flight paths. ETL, SageMaker pipelines, CI/CD development AWS SageMaker, Apache Airflow, Model deployment, AWS, PySpark, Airflow

NLP, Time Series Forecast, Deep learning, Model Deployment OCR, Document Classification, Contract Element Extraction, Named Entities Recognition, Document Summary Cognitive Services, MLOps, Azure ML, Synapsis Analitics, Databricks, Pyspark