AI & Data Engineering Internship

Not Disclosed
In-Office
15 Apr 2026

AI & ML

Internship

15 Apr 2026

AI & Data Engineering Intern Role at Anblicks

Anblicks is hiring for the role of AI & Data Engineering Intern, a position centered on data pipelines, SQL, Python, and cloud-based data processing. The role focuses on supporting AI-driven analytics and machine learning use cases through structured and semi-structured data work. It also includes exposure to Snowflake and Databricks, along with practical work in data ingestion, transformation, and feature preparation. The requirement is specific: candidates must hold a Bachelor’s degree in Engineering in fields such as Computer Science, IT, Data Science, or related areas, and the opportunity is for Only 2026 Passouts. A basic understanding of SQL and Python is also required.

What the Internship Focuses On

The internship is built around the practical work needed to support modern data and AI workflows. At its core, the role asks the intern to assist in building and maintaining data pipelines that help power analytical and machine learning tasks. This means the work is not limited to one tool or one stage of the process. Instead, it spans the movement, preparation, and organization of data so it can be used effectively for AI-related outcomes.

The responsibilities also make it clear that the intern will work with both structured and semi-structured data. That includes ingestion, transformation, and preparation for analytical and AI workloads. These tasks are important because they connect raw data to usable data, which is essential for analytics and machine learning use cases. The role therefore sits at the intersection of data engineering and AI support, with a practical emphasis on making data ready for downstream work.

Standout focus: the internship is not only about learning tools, but about contributing to the data foundation that supports AI-driven analytics and machine learning use cases.

Core focus areas in the role

Building and maintaining data pipelines
Supporting AI-driven analytics and machine learning use cases
Working with structured and semi-structured data
Handling data ingestion, transformation, and preparation
Preparing data for analytical and AI workloads

The role also suggests a workflow-oriented environment where each responsibility connects to the next. Data is ingested, transformed, prepared, and then used for analytical or AI purposes. This makes the internship especially relevant for someone who wants exposure to the full path from data handling to AI-ready preparation. The responsibilities are broad enough to provide meaningful hands-on learning, while still being clearly defined around data engineering and AI support.

Data Pipelines, Ingestion, and Preparation Work

A major part of the internship is the opportunity to assist in building and maintaining data pipelines. These pipelines support the movement and organization of data so it can be used in analytics and machine learning contexts. The role indicates that the intern will contribute to the operational side of data work, where reliability and preparation matter. This is a practical responsibility because pipelines are central to keeping data available and usable for AI-related tasks.

The internship also includes work on structured and semi-structured data ingestion. That means the intern will be involved in bringing different forms of data into a usable environment. After ingestion, the data must be transformed and prepared for analytical and AI workloads. These steps are important because they help convert incoming data into a form that can support further processing and analysis. The description highlights these tasks as part of the intern’s hands-on responsibilities rather than as abstract learning topics.

Because the role includes both ingestion and transformation, it naturally involves attention to detail and consistency. The intern will be working with data that needs to be prepared correctly before it can support analytics or machine learning use cases. The work is therefore connected to the quality and readiness of the data itself. In this sense, the internship offers exposure to the practical side of data engineering that underpins AI-driven outcomes.

Data workflow responsibilities

Assist in building data pipelines
Assist in maintaining data pipelines
Work with structured data ingestion
Work with semi-structured data ingestion
Support data transformation
Support data preparation for analytical and AI workloads

The responsibilities show a clear sequence of work, from ingestion to transformation to preparation. Each step contributes to the larger goal of supporting AI-driven analytics and machine learning use cases. The role is therefore suitable for someone who wants direct exposure to how data is handled before it reaches analytical or AI workflows. It is a practical internship with a strong emphasis on foundational data engineering tasks.

SQL, Snowflake, and Python in the Internship

The internship places clear importance on SQL and Python. The responsibilities specifically mention writing, optimizing, and troubleshooting SQL queries, including work with the Snowflake cloud data warehouse. This means the intern will not only write queries but also improve and debug them as part of the role. The mention of Snowflake shows that the work includes cloud data warehouse exposure as part of the SQL-related tasks.

Python is also a key part of the internship. The role states that Python will be used for data processing, transformation, and feature preparation for AI/ML workflows. This connects Python directly to the data preparation side of the internship and to the machine learning use cases mentioned in the responsibilities. The language is therefore positioned as a practical tool for preparing data for AI-related work rather than as a standalone learning topic.

The requirements also mention a basic understanding of SQL and Python. This is important because it shows the internship expects some foundational familiarity before the intern begins. At the same time, the responsibilities suggest that the role will provide hands-on exposure and practical application. The combination of basic knowledge and active use makes the internship relevant for candidates who want to strengthen their skills through real work.

Tools and technical tasks mentioned

Write SQL queries
Optimize SQL queries
Troubleshoot SQL queries
Work with Snowflake cloud data warehouse
Use Python for data processing
Use Python for transformation
Use Python for feature preparation for AI/ML workflows

The role’s technical scope is focused and practical. SQL is tied to query work and Snowflake, while Python is tied to processing, transformation, and feature preparation. Together, these skills support the broader data pipeline and AI workload responsibilities described in the internship. The result is a role that connects core programming and data handling skills to real data engineering tasks.

Databricks Exposure and Scalable Data Processing

Another important part of the internship is the chance to gain hands-on exposure to Databricks. The description specifically mentions Apache Spark fundamentals, which indicates that the intern will be introduced to scalable data processing concepts. This exposure is tied to the broader data engineering responsibilities of the role and supports the work involved in handling data for analytics and AI use cases.

The mention of Databricks suggests that the internship includes practical learning in an environment associated with scalable processing. Since the role already includes data pipelines, ingestion, transformation, and preparation, Databricks fits naturally into that workflow. It adds another layer to the intern’s experience by connecting data handling with scalable processing fundamentals. The focus remains on hands-on exposure rather than theoretical coverage alone.

Apache Spark fundamentals are specifically named, which helps define the kind of learning involved. The role does not expand beyond that, so the safe interpretation is that the intern will gain exposure to the basics of scalable processing through Databricks. This makes the internship relevant for someone interested in understanding how data processing can be handled at scale within AI and analytics contexts.

Gain hands-on exposure to Databricks, including Apache Spark fundamentals, for scalable data processing.

Why this part of the role matters

Connects data engineering work with scalable processing
Supports the handling of data for AI and analytics use cases
Introduces Databricks as part of the internship experience
Includes Apache Spark fundamentals

Databricks exposure adds depth to the internship because it complements the SQL and Python responsibilities already listed. It also reinforces the role’s focus on practical data engineering rather than isolated tool usage. For a candidate interested in AI and data engineering, this part of the internship shows that the work extends into scalable processing concepts as well. The learning path is clearly tied to the responsibilities already outlined in the role.

Requirements and Candidate Fit

The requirements for the role are specific and limited to the information provided. Candidates must have a Bachelor’s degree in Engineering, with acceptable fields including Computer Science, IT, Data Science, or related fields. The role is also open only to 2026 Passouts. These requirements define the intended candidate profile clearly and leave no ambiguity about eligibility.

In addition to the degree requirement, the internship asks for a basic understanding of SQL and Python. This aligns closely with the responsibilities, which include SQL query work and Python-based data processing. The requirement suggests that the intern should already have foundational knowledge before joining, so they can contribute to the tasks described in the role. The emphasis is on readiness for practical work in a data engineering and AI support environment.

The candidate fit is therefore centered on engineering students with a data or computing background who are preparing to graduate in 2026. The role is not described as requiring advanced experience, but it does expect familiarity with the core tools used in the internship. That balance makes the opportunity focused and accessible to the intended group, while still being grounded in real technical responsibilities.

Eligibility details mentioned

Bachelor’s degree in Engineering
Computer Science, IT, Data Science, or related fields
Only 2026 Passouts
Basic understanding of SQL
Basic understanding of Python

The requirements and responsibilities work together to define the role clearly. The internship is aimed at candidates who can support data pipeline work, SQL tasks, Python-based preparation, and exposure to Databricks. Because the eligibility is specific, it is important to match the stated criteria exactly. The role is designed for a defined academic and skill background, with a strong focus on practical data engineering work.

Frequently Asked Questions

What is the role being offered by Anblicks?

Anblicks is hiring for the role of AI & Data Engineering Intern. The position focuses on supporting AI-driven analytics and machine learning use cases through data pipelines, data ingestion, transformation, and preparation. It also includes SQL, Python, Snowflake, and Databricks exposure.

What kind of data work is included in the internship?

The internship includes assisting in building and maintaining data pipelines, working with structured and semi-structured data ingestion, and handling data transformation and preparation. These tasks are meant to support analytical and AI workloads. The role is centered on practical data engineering work.

Which tools and technologies are mentioned in the responsibilities?

The responsibilities mention SQL, Snowflake cloud data warehouse, Python, and Databricks. SQL is used for writing, optimizing, and troubleshooting queries. Python is used for data processing, transformation, and feature preparation, while Databricks exposure includes Apache Spark fundamentals.

Who is eligible for this internship?

The requirements specify a Bachelor’s degree in Engineering in Computer Science, IT, Data Science, or related fields. The role is for Only 2026 Passouts. A basic understanding of SQL and Python is also required.

Does the internship mention machine learning work?

Yes, the responsibilities mention supporting machine learning use cases. The role also includes preparing data and features for AI/ML workflows. This shows that the internship is connected to both analytics and machine learning support through data engineering tasks.

Is Databricks part of the internship?

Yes, the internship includes hands-on exposure to Databricks. The description specifically mentions Apache Spark fundamentals for scalable data processing. This exposure is part of the role’s broader focus on data pipelines and AI-related data work.

Conclusion

Anblicks’ AI & Data Engineering Intern role is clearly focused on practical work in data pipelines, data ingestion, transformation, SQL, Python, Snowflake, and Databricks. The internship is designed to support AI-driven analytics and machine learning use cases through hands-on data engineering tasks. Its requirements are equally specific, calling for a Bachelor’s degree in Engineering in relevant fields, basic SQL and Python understanding, and eligibility limited to 2026 Passouts. For candidates who match the stated profile, the role offers direct exposure to the data foundation behind AI and analytics work.

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