Data & Machine Learning Engineer

IDT Corporation is a global communications company providing prepaid communication, payment, money transfer, international calling, and mobile top-up services. The company is seeking a Data and Machine Learning Engineer to design, build, and maintain end-to-end data pipelines and architecture supporting data warehouses, LLM-driven applications, and AI-based business intelligence. The role also involves MLOps, semantic retrieval, vector databases, model workflows, documentation, and collaboration with data stakeholders.

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

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
• Applicants must be from LATAM
• Please attach CV in English
• 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