Senior Data/ML Engineer

Summary

Join IDT, a leading telecommunications company, as a full-time, work-from-home Data/ML Engineer based in LATAM. You will be part of the BI team, designing, building, and maintaining the data pipeline architecture for our data warehouse, LLM-driven applications, and AI-based BI. This role requires expertise in data engineering, MLOps, and LLMs. You will develop scalable data pipelines, optimize workflows for unstructured data, and build analytics and dashboarding solutions. The ideal candidate will have extensive experience with big data technologies, cloud platforms, and relational databases. This position offers maximum location flexibility.

Requirements

• 8+ years of experience as a Data Engineer with 2+ years focused on MLOps
• Excellent English communication skills
• Effective oral and written communication skills with BI team and user community
• Demonstrated experience in utilizing python for data engineering tasks, including transformation, advanced data manipulation, and large-scale data processing
• Deep understanding of vector databases and RAG architectures, and how they drive semantic retrieval workflows
• Skilled at integrating open-source LLM frameworks into data engineering workflows for end-to-end model training, customization, and scalable inference
• Experience with cloud platforms like AWS or Azure Machine Learning for managed LLM deployments
• Hands-on experience with big data technologies including Apache Spark, Hadoop, and Kafka for distributed processing and real-time data ingestion
• Experience designing complex data pipelines extracting data from RDBMS, JSON, API and Flat file sources
• Demonstrated skills in SQL and PLSQL programming, with advanced mastery in Business Intelligence and data warehouse methodologies, along with hands-on experience in one or more relational database systems and cloud-based database services such as Snowflake/Redshift
• Understanding of software engineering principles and skills working on Unix/Linux/Windows Operating systems, and experience with Agile methodologies
• Proficiency in version control systems, with experience in managing code repositories, branching, merging, and collaborating within a distributed development environment
• Interest in business operations and comprehensive understanding of how robust BI systems drive corporate profitability by enabling data-driven decision-making and strategic insights

Responsibilities

• Design, develop, and maintain scalable data pipelines to support ingestion, transformation, and delivery into centralized feature stores, model-training workflows, and real-time inference services
• Build and optimize workflows for extracting, storing, and retrieving semantic representations of unstructured data to enable advanced search and retrieval patterns
• Architect and implement lightweight analytics and dashboarding solutions that deliver natural language query experience and AI-backed insights
• Define and execute processes for managing prompt engineering techniques, orchestration flows, and model fine-tuning routines to power conversational interfaces
• Oversee vector data stores and develop efficient indexing methodologies to support retrieval-augmented generation (RAG) workflows
• Partner with data stakeholders to gather requirements for language-model initiatives and translate into scalable solutions
• Create and maintain comprehensive documentation for all data processes, workflows and model deployment routines
• Should be willing to stay informed and learn emerging methodologies in data engineering, MLOps and LLM operations

Preferred Qualifications

• Experience with vector databases such as DataStax AstraDB, and developing LLM-powered applications using popular open source frameworks like LangChain and LlamaIndex–including prompt engineering, retrieval-augmented generation (RAG), and orchestration of intelligent workflows
• Familiarity with evaluating and integrating open-source LLM frameworks–such as Hugging Face Transformers/LLaMA-4 across end-to-end workflows, including fine-tuning and inference optimization
• Knowledge of MLOps tooling and CI/CD pipelines to manage model versioning and automated deployments