What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

The rise of AI and GenAI has brought about the rise of new questions in the data ecosystem – and new roles. One job that has become increasingly popular across enterprise data teams is the role of the AI data engineer. 

Demand for AI data engineers has grown rapidly in data-driven organizations. But what does an AI data engineer do? What are they responsible for? What skills do they need?

Below, we’ll break down the key aspects of this role.

What Does an AI Data Engineer Do?

AI data engineers are data engineers that are responsible for developing and managing data pipelines that support AI and GenAI data products. Their work bridges traditional data engineering with the specific requirements of artificial intelligence applications, ensuring models receive the high-quality data necessary for optimal performance.

Core Responsibilities of an AI Data Engineer

AI data engineers have several responsibilities related to AI data products, including:

• Building and maintaining data pipelines for AI and machine learning models
• Developing and managing data architectures that support AI workflows
• Integrating and preparing large-scale datasets for AI and ML model training.

Key Differences Between AI Data Engineers and Traditional Data Engineers

While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. 

AI data engineers tend to focus primarily on AI, generative AI (GenAI), and machine learning (ML)-specific needs, like handling unstructured data and supporting real-time analytics. They still take on the responsibilities of a traditional data engineer, like building and managing pipelines and maintaining data quality, but they are tasked with delivering AI data products, rather than traditional data products. 

The foundational skills are similar between traditional data engineers and AI data engineers are similar, with AI data engineers more heavily focused on machine learning data infrastructure, AI-specific tools, vector databases, and LLM pipelines. Let’s dive into the tools necessary to become an AI data engineer.

Essential Skills for AI Data Engineers

Expertise in Data Pipelines and ETL Processes

A foundational skill for data engineers? The ability and skills to build scalable, automated data pipelines. That means you need to know crucial ETL and ELT processes to extract, transform, and load data not only for traditional data pipelines, but for pipelines supporting AI and ML models as well. 

Knowledge of AI and Machine Learning Frameworks

In order to effectively build and manage AI pipelines, an AI data engineer needs to be familiar with machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn, as well as LLM orchestration tools like LangChain and LlamaIndex. These frameworks are used to bring AI agents into production, develop RAG applications, and support research initiatives. Additionally, they must be familiar with the challenges of these non-deterministic systems and approaches, such as agent observability, for monitoring their reliability.

A solid understanding of these ML frameworks will enable an AI data engineer to effectively collaborate with data scientists to optimize AI model performance, implement efficient parameter tuning, and improve scale through techniques like model quantization and distillation.

Proficiency in Programming Languages

Knowledge of programming languages is a must for AI data engineers and traditional data engineers alike. Both traditional and AI data engineers should be fluent in SQL for managing structured data, but AI data engineers should be proficient in NoSQL databases as well for unstructured data management. 

In addition, AI data engineers should be familiar with programming languages such as Python, Java, Scala, and more for data pipeline, data lineage, and AI model development. 

Big Data and Cloud Infrastructure Knowledge

Lastly, AI data engineers should be comfortable working with distributed data processing frameworks like Apache Spark and Hadoop, as well as cloud platforms like AWS, Azure, and Google Cloud. 

Enterprise data organizations might run on any of these on-prem and cloud infrastructures – including combinations of them – so it’s essential to be familiar with as many as possible.

Tools and Technologies AI Data Engineers Use

In order to learn and execute the skills mentioned above, AI data engineers need to be familiar with several tools to be considered for a role, including:

AI and Machine Learnning Tools

These include several tools and frameworks for developing and deploying AI models, including TensorFlow, PyTorch, Keras, as well as LLM-specific tools like Hugging Face Transformers, LangChain, and vector database systems such as Pinecone, Weaviate, and Milvus for efficient similarity search and retrieval.

Data Engineering Tools

Data engineers need to be comfortable using essential tools for data pipeline management and workflow orchestration, including Apache Kafka, Apache Spark, Airflow, Dagster, dbt, and many more. Modern AI workflows also require familiarity with feature stores like Feast or Tecton that bridge ML development and production environments. There are dozens of data engineering tools available on the market, so familiarity with a wide variety of these can increase your attractiveness as an AI data engineering candidate.

Data Storage Solutions

As we all know, data can be stored in a variety of ways. Get familiar with data warehouses, data lakes, and data lakehouses, including MongoDB, Cassandra, BigQuery, Redshift and more. Familiarity with data warehouses that offer scalable storage are especially important for AI data engineers.

Data monitoring and Data Observability Tools

Real-time monitoring for data pipelines supporting AI models is extremely important. AI data engineers should have a solid understanding of and familiarity with data observability tools, like Monte Carlo, to ensure the quality and accuracy of the pipelines and outputs of your AI and ML models.

The Role of AI Data Engineers in Supporting AI and ML Models

AI data engineers are responsible for a few key initiatives in their roles, including the following list.

Ensuring Data Quality for AI Models

Just as traditional data engineers are responsible for delivering accurate data, AI data engineers are also responsible for delivering data that’s clean, structured, accurate, and reliable for AI. Because AI data engineers are the developers and managers of the AI data pipeline, it’s their responsibility to ensure the data that’s flowing through those pipelines is trustworthy and accurate. If it’s not, then inaccurate, outdated, or stale data might make its way into an AI model – resulting in inaccurate outputs and diminishing performance.

Data + AI observability solutions are one approach to ensuring high quality data and high quality models. Check out the video on the Troubleshooting Agent below to see how these platforms can automatically identify the root cause of specific issues so they are resolved before they impact AI models.

Collaborating with Data Scientists

AI data engineers have the unique ability to enable data scientists by providing the pipeline infrastructure they need to train, test, and deploy AI models efficiently. 

Together with AI data engineers, data scientists can be better prepared to build trustworthy, reliable GenAI and AI models for both internal and external stakeholders. 

Real-Time Data Streaming and AI Applications

Streaming and real-time data are essential for many AI applications. In order to provide real-time recommendations or predictive analytics, data needs to be updated as its created to ensure responses are as up-to-date and accurate as possible.

AI data engineers are responsible for building data pipelines and infrastructure that support real-time data for AI-powered insights and applications. 

Challenges Faced by AI Data Engineers

Just because “AI” involved doesn’t mean all the challenges go away! In fact, AI data engineers encounter many – and sometimes even more complex – challenges than traditional data engineers. Let’s examine a few.

Handling Large, Unstructured Data

AI data engineers are often faced with the myriad complexities that come with managing and preparing massive, diverse datasets for machine learning and AI applications. Beyond text, today’s AI systems increasingly process images, audio, and video data simultaneously. This might mean having to rethink or redesign some of your data architecture to make room for a semantic layer, vector database, and multimodal embedding systems that LLMs can leverage to make sense of unstructured data from various sources. Managing the compute requirements and latency constraints of these systems presents additional engineering challenges.

Ensuring Data Pipeline Reliability

As a data organization scales, AI data engineers are also responsible for demystifying the challenges that come with scaling data pipelines for AI workloads. In a growing organization, data drift is more frequent, and AI data engineers need to be cognizant if it happens and fix it right away. AI data engineers are the first line of defense against unreliable data pipelines that serve AI models.

Data Governance, Compliance, and Responsible AI

AI data engineers work closely with data governance, compliance, and security teams to ensure that all security regulations are abided by when building AI systems. This now includes navigating emerging AI-specific regulations like the EU AI Act, addressing algorithmic bias, implementing model monitoring for fairness, and designing explainability mechanisms. Beyond traditional PII concerns, AI data engineers must implement prompt engineering guardrails, content filtering, and responsible synthetic data generation practices. While ensuring data is secure, ethical, and compliant can be a long process, it’s essential for trustworthy AI — and AI data engineers are at the helm of this evolving discipline.

The Importance of Data Observability for AI Data Engineers

AI data engineers benefit greatly from tools like data observability, which provide end-to-end data monitoring to enable them to detect, triage, and resolve data issues quickly. 

Especially when dealing with AI pipelines, AI data engineers require a clear understanding of pipeline health at every stage so they can identify anomalies and resolve data quality issues before they get to the model. As we all know, garbage data in, garbage data out. 

Data observability provides visibility into the health of AI pipelines, enabling AI data engineers to proactively identify data drift, get alerted to changes in inputs, and maintain the reliability of the data, ultimately preventing AI model failures.

Ensure Data Quality with Monte Carlo’s Data Observability Platform

At the end of the day, AI data engineers need reliable, high-quality data pipelines to ensure their models perform at their best. Monte Carlo’s data observability platform helps you proactively monitor and maintain data quality, resolve anomalies, and prevent data drift in real-time. 

Try Monte Carlo today and empower your AI workflows with accurate and reliable data.

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