AI & Data Engineer

Accenture Nordics; København; On-site; DKK 900,000 - 1,200,000; Full time

Job summary

Accenture Nordics is seeking an AI & Data Engineer to join our Digital Core – AI & Data team in Denmark. You will build data pipelines and platforms that analytics and AI run on, while delivering GenAI-powered features and applications using LLMs, RAG, and agent-based approaches.

You will work with data engineers, data architects, and AI specialists to solve business challenges across industries, developing analytical models, AI solutions, and actionable insights with measurable impact.

Qualifications

• Bachelor's or Master's in CS, SE, Data Eng, Data Science, Math, Physics, Engineering, or related field.
• Strong Python/SQL and data engineering fundamentals.
• Experience building data pipelines and AI/ML systems.
• Knowledge of cloud data platforms and MLOps.

Responsibilities

• Build, test, and maintain data pipelines ingesting data from diverse sources.
• Prepare, clean, and model data for analytics, ML, and Generative AI use cases.
• Develop on cloud platforms using Databricks, ADF, Snowflake, Fabric.
• Work with Spark and Kafka for scalable data processing.
• Build GenAI features: RAG, prompts, agent workflows, LLM API integrations.
• Support deployment and monitoring of AI/ML models in production (MLOps).
• Contribute to data quality, validation, and governance.
• Collaborate with stakeholders to translate data/AI needs into solutions.
• Stay current on data engineering and GenAI tooling.

Skills

Python

SQL

LLMs

Data pipelines

Cloud platforms

Databricks

Spark

Kafka

Git

Docker

CI/CD

Terraform

dbt

TensorFlow

PyTorch

Education

Bachelor's or Master's in CS/SE/Data Engineering/Data Science/Math/Engineering

Tools

Databricks

Azure Data Factory

Snowflake

Fabric

Spark

Kafka

Airflow

dbt

Terraform

Git

Docker

Job description

We're looking for an AI & Data Engineer to join our Digital Core – AI & Data team. This role is ideal for someone who wants to build data pipelines and platforms that analytics and AI run on, as well as hands-on building AI-powered features and applications using LLMs, RAG, and agent-based approaches.

As part of a multidisciplinary team, you'll work alongside data engineers, data architects, cloud/DevOps engineers, and AI specialists to help clients solve complex business challenges using data and AI. You will support the development of analytical models, AI solutions, and insights that drive innovation and measurable outcomes across industries.

What You Will Do

• Build, test, and maintain data pipelines that ingest, move, and transform data from a wide range of source systems.
• Prepare, clean, and model data so it's reliable and ready for analytics, reporting, machine learning, and Generative AI use cases.
• Develop on cloud data platforms such as Azure, AWS, or Google Cloud, using services like Databricks, Azure Data Factory, Snowflake, Fabric, or their equivalents.
• Work with distributed processing and streaming tools such as Spark and Kafka to handle data at scale.
• Build and integrate GenAI-powered features: RAG pipelines, prompt engineering, agent workflows, and API integration with LLM providers (e.g. OpenAI, Anthropic, Azure OpenAI).
• Support deployment and monitoring of AI/ML models in production (MLOps), working alongside DevOps and platform engineers.
• Contribute to data quality, validation, and monitoring so the data teams depend on can be trusted.
• Support deployment of data and AI solutions using version control, CI/CD, and infrastructure-as-code.
• Collaborate with colleagues and client/business stakeholders to translate data and AI requirements into working technical solutions.
• Stay current on developments in data engineering, cloud platforms, and GenAI/LLM tooling.

Who You Are

You're curious, hands-on, and like understanding how things work under the hood. You like writing code, solving technical problems, and seeing systems you built run reliably. You are eager to learn new tools and technologies and enjoy working as part of a team.

You may have gained experience through:

• 2–3 years of hands-on experience in data engineering or applied AI/ML engineering.
• University coursework, thesis work, internships, or graduate programmes focused on computer science, software engineering, data engineering, or related fields.
• Personal or academic projects where you built something with data: a pipeline, a database, an API, or an application running in the cloud.
• Exposure to cloud platforms, open-source data tools, or software development practices.

Qualifications

Must-Have

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Data Science, Mathematics, Physics, Engineering, or a related field.
• Solid programming skills in Python and SQL (Scala or Java a plus).
• Hands-on experience building with LLMs — e.g. RAG pipelines, prompt engineering, agent frameworks, or integrating LLM provider APIs into applications.
• Working knowledge of core data concepts: relational databases, data modeling, ETL/ELT processes.
• Strong, structured problem-solving and debugging skills.
• Ability to explain technical work clearly to both technical and non-technical colleagues.
• Strong teamwork and collaboration skills.
• Fluency in English (Danish language skills are an advantage for local roles).

Nice-to-Have

• Hands-on experience with a cloud platform (Azure, AWS, or Google Cloud), ideally including data services.
• Experience with Spark, Databricks, Kafka, Airflow, dbt, or similar data engineering tools.
• Familiarity with Git, Docker, CI/CD pipelines, or infrastructure-as-code tools such as Terraform.
• Experience with machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, or similar.
• Interest in data architecture concepts such as lakehouses, medallion architecture, data mesh, or data governance.
• Relevant certifications (e.g. Databricks Data Engineer Associate, AWS Data Engineer, Claude etc.).