Events & Community Growth Intern

Innodata Inc.•Washington, DC

About The Position

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: We are looking for a curious and driven Data Engineering Intern to join our Data & AI team. You will primarily focus on building and maintaining robust data pipelines and infrastructure, while also contributing to applied AI projects involving Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. This is a hands-on role. You will work alongside senior engineers and data scientists, contribute to production-grade systems. The role is roughly 65% Data Engineering and 35% Data Science / Applied AI.

Requirements

Pursuing a degree in Computer Science, Data Science, Engineering, or a related field
Solid foundation in Python — comfortable writing clean, modular, production-quality code
Hands-on experience with SQL (query optimization, CTEs, window functions)
Familiarity with at least one cloud platform — AWS, GCP, or Azure
Understanding of data pipeline concepts: batch vs streaming, orchestration, idempotency
Strong analytical mindset with attention to data quality and correctness
Experience with workflow orchestrators: Apache Airflow, Prefect, or Dagster
Exposure to dbt for data transformation and testing

Responsibilities

Design, build, and maintain scalable ETL/ELT data pipelines using tools like Apache Airflow, dbt, or Spark
Work with structured and unstructured data from various sources — APIs, databases, event streams
Write optimized SQL queries and data transformation logic for analytical and ML use cases
Maintain and improve data quality, schema management, and pipeline monitoring
Collaborate on data warehouse and data lake architecture (e.g., Snowflake, BigQuery, Delta Lake)
Document data flows, lineage, and schema definitions
Build and evaluate RAG pipelines — chunking, embedding, indexing, and retrieval
Work with vector databases (e.g., Pinecone, Weaviate, pgvector) for semantic search
Integrate LLM APIs (OpenAI, Anthropic, open-source models) into data products or internal tools
Help with prompt engineering, evaluation frameworks, and fine-tuning experiments
Support exploratory data analysis and feature engineering for ML workflows