IA & Data Engineer M/F

Join AFD.Tech, part of Accenture

AFD.Tech, part of Accenture, has established itself as a market leader by supporting major organizations in their digital transformation. As part of Accenture, a global consulting leader with more than 800,000 people worldwide, AFD.Tech combines multisector expertise with the scale and capabilities of an international group.

AFD.Tech helps clients address today’s technology challenges and anticipate those of tomorrow. Its integration into Accenture in 2022 marked a key milestone in its growth, strengthening its presence and capabilities in strategic areas including networks, cloud and cybersecurity.

Role overview

We are looking for a versatile Data Engineer / AI Engineer to design, build and maintain modern data platforms while contributing to the development and industrialization of Artificial Intelligence and Machine Learning solutions.

You will work across different client environments and industries, collaborating with Data Scientists, Data Analysts, Software Engineers, Architects and business stakeholders.

The ideal candidate combines strong Data Engineering fundamentals with practical exposure to AI, Machine Learning and Generative AI, and is comfortable working with modern cloud-based data ecosystems.

Key responsibilities:

Data Engineering

· Design, develop and maintain scalable data pipelines and ETL/ELT processes.

· Collect, transform and integrate data from multiple structured and unstructured sources.

· Build and maintain data models and architectures supporting analytics and AI use cases.

· Ensure data quality, reliability, performance and traceability.

· Develop batch and real-time data processing solutions.

· Optimize data pipelines and processing workloads.

· Contribute to data platform architecture and technical design.

· Work closely with Data Scientists and Data Analysts to provide reliable and accessible datasets.

AI & Machine Learning

· Contribute to the development and deployment of Machine Learning and AI solutions.

· Prepare and transform datasets for Machine Learning applications.

· Support the industrialization and deployment of ML models.

· Develop APIs or services to expose AI capabilities to applications.

· Contribute to Generative AI use cases, including LLM-based applications, RAG architectures, embeddings and vector databases.

· Implement monitoring and continuous improvement mechanisms for AI solutions.

· Collaborate with business stakeholders to translate concrete business challenges into Data & AI solutions.

Technical skills

Data Engineering fundamentals

· Python

· SQL

· ETL / ELT pipelines

· Data modeling

· Data Warehousing and Data Lakes

· REST APIs and data integration

Data platforms and tooling

Experience with one or more of the following is highly valued:

· Databricks

· Apache Spark / PySpark

· Microsoft Fabric

· Snowflake

· Apache Kafka

· Airflow

· dbt

Cloud

Experience with at least one major cloud ecosystem is required. Expertise across all three providers is not expected.

· Microsoft Azure: Azure Data Factory, Azure Data Lake Storage, Synapse Analytics and Azure Databricks.

· AWS: S3, Glue, Lambda, Redshift and SageMaker.

· Google Cloud Platform: BigQuery, Dataflow and Vertex AI.

AI / Machine Learning

· Machine Learning concepts and algorithms.

· Python data ecosystem: Pandas, NumPy and Scikit-learn.

· ML model deployment and APIs.

· MLOps principles.

· Azure Machine Learning, MLflow or equivalent tooling.

Exposure to Generative AI technologies is a strong asset:

· Large Language Models (LLMs).

· Retrieval-Augmented Generation (RAG).

· Prompt engineering.

· Embeddings and vector databases.

· Azure OpenAI / OpenAI.

· LangChain or similar frameworks.

DevOps & Software Engineering

· Git and CI/CD pipelines.

· Docker and Kubernetes.

· Infrastructure as Code; Terraform is an asset.

· Agile / Scrum delivery environments.