IA & Data Engineer M/F

h3Join AFD.
Tech, part of Accenture /h3 pAFD.
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. /p pAFD.
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. /p h3Role overview /h3 pWe 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. /p pYou will work across different client environments and industries, collaborating with Data Scientists, Data Analysts, Software Engineers, Architects and business stakeholders. /p pThe 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. /p h3Key responsibilities /h3 h3Data Engineering /h3 ul liDesign, develop and maintain scalable data pipelines and ETL/ELT processes. /li liCollect, transform and integrate data from multiple structured and unstructured sources. /li liBuild and maintain data models and architectures supporting analytics and AI use cases. /li liEnsure data quality, reliability, performance and traceability. /li liDevelop batch and real-time data processing solutions. /li liOptimize data pipelines and processing workloads. /li liContribute to data platform architecture and technical design. /li liWork closely with Data Scientists and Data Analysts to provide reliable and accessible datasets. /li /ul h3AI Machine Learning /h3 ul liContribute to the development and deployment of Machine Learning and AI solutions. /li liPrepare and transform datasets for Machine Learning applications. /li liSupport the industrialization and deployment of ML models. /li liDevelop APIs or services to expose AI capabilities to applications. /li liContribute to Generative AI use cases, including LLM-based applications, RAG architectures, embeddings and vector databases. /li liImplement monitoring and continuous improvement mechanisms for AI solutions. /li liCollaborate with business stakeholders to translate concrete business challenges into Data AI solutions. /li /ul h3Technical skills /h3 h3Data Engineering fundamentals /h3 ul liPython /li liSQL /li liETL / ELT pipelines /li liData modeling /li liData Warehousing and Data Lakes /li liREST APIs and data integration /li /ul h3Data platforms and tooling /h3 pExperience with one or more of the following is highly valued: /p ul liDatabricks /li liApache Spark / PySpark /li liMicrosoft Fabric /li liSnowflake /li liApache Kafka /li liAirflow /li lidbt /li /ul h3Cloud /h3 pExperience with at least one major cloud ecosystem is required. Expertise across all three providers is not expected. /p ul liMicrosoft Azure: Azure Data Factory, Azure Data Lake Storage, Synapse Analytics and Azure Databricks. /li liAWS: S3, Glue, Lambda, Redshift and SageMaker. /li liGoogle Cloud Platform: BigQuery, Dataflow and Vertex AI. /li /ul h3AI / Machine Learning /h3 ul liMachine Learning concepts and algorithms. /li liPython data ecosystem: Pandas, NumPy and Scikit-learn. /li liML model deployment and APIs. /li liMLOps principles. /li liAzure Machine Learning, MLflow or equivalent tooling. /li /ul pExposure to Generative AI technologies is a strong asset: /p ul liLarge Language Models (LLMs). /li liRetrieval-Augmented Generation (RAG). /li liPrompt engineering. /li liEmbeddings and vector databases. /li liAzure OpenAI / OpenAI. /li liLangChain or similar frameworks. /li /ul h3DevOps Software Engineering /h3 ul liGit and CI/CD pipelines. /li liDocker and Kubernetes. /li liInfrastructure as Code; Terraform is an asset. /li liAgile / Scrum delivery environments. /li /ul h3Profile /h3 ul li3+ years of experience in Data Engineering, AI/ML Engineering or a related role. /li liStrong proficiency in Python and SQL. /li liExperience building production-grade data pipelines. /li liExperience with cloud-based data platforms. /li liGood understanding of software engineering best practices. /li liStrong analytical and problem-solving skills. /li liAbility to communicate effectively with technical and non-technical stakeholders. /li liAutonomous, proactive and comfortable working in different client environments. /li /ul h3Nice to have /h3 ul liExperience with Generative AI and LLM-based applications. /li liKnowledge of Data Governance and Data Quality principles. /li liExperience with real-time or event-driven architectures. /li liExperience delivering producti