Data & Machine Learning Engineer

About the role

We are seeking a senior Data & Machine Learning Engineer to join our Business Intelligence team. You will design, build, and maintain end‑to‑end data pipelines, feature stores and LLM‑driven applications for a global telecommunications leader. The role offers maximum flexibility to work from anywhere in LATAM, with occasional in‑person verification.

Key responsibilities

• Design, develop and maintain scalable data pipelines for ingestion, transformation and delivery to feature stores, model‑training workflows and real‑time inference services.
• Build and optimise workflows that extract, store and retrieve semantic representations of unstructured data for advanced search and retrieval.
• Architect lightweight analytics and dashboarding solutions that provide natural‑language query experiences and AI‑backed insights.
• Define and execute processes for prompt engineering, orchestration flows and model fine‑tuning to power conversational interfaces.
• Oversee vector data stores and implement efficient indexing methods to support retrieval‑augmented generation (RAG) workflows.
• Collaborate with data stakeholders to gather requirements for language‑model initiatives and translate them into scalable solutions.
• Create and maintain comprehensive documentation for data processes, workflows and model deployment routines.
• Stay informed on emerging methodologies in data engineering, MLOps and LLM operations.

Required profile

• 8+ years of experience as a Data Engineer, including at least 2 years focused on MLOps.
• Excellent English communication skills, both oral and written.
• Proven ability to work with BI teams and end‑users to translate business needs into technical solutions.

Required skills

• Python for data engineering and large‑scale data processing.
• Experience with vector databases and RAG architectures.
• Integration of open‑source LLM frameworks into data pipelines.
• Cloud platforms such as AWS or Azure Machine Learning for managed LLM deployments.
• Big data technologies including Apache Spark, Hadoop and Kafka.
• Designing complex data pipelines extracting data from RDBMS, JSON, APIs and flat‑file sources.
• Strong knowledge of MLOps practices and model deployment workflows.