Architect (AI, Cloud, Data)

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

You will be a part of our Insights & Data practice, and member of a team that delivers cutting-edge data centric solutions. Most of our projects are Cloud & Big Data engineering. We develop solutions to process large, also unstructured, datasets, with custom build Cloud Data services on AWS, Azure or GCP.

We are responsible for the full SDLC of the solution: apart from using data processing tools (e.g. ETL), we code a lot in Python, Scala or Java and use DevOps tools and best practices. The data is either exposed to downstream systems via API, outbound interfaces or visualized on reports and dashboards.

• Creating architecture for systems processing large and unstructured datasets (Data Lake Architecture, Streaming Architecture), including AI-ready data platforms supporting machine learning and Generative AI workloads;

• Designing modern DWH / Big Data solutions in the cloud and in the Continuous Delivery / Continuous Integration environment;

• Designing and integrating AI/ML and Generative AI solutions (e.g. LLM-based use cases) into cloud-based data platforms;

• Data model design, data flow documentation;

• Supporting the Team as a Technical Leader, ensuring system compliance with standards and best practices in software development.

• Good experience as an Architect and/or Technical Leader in Cloud or Big Data projects in the field of data processing and visualization (in different phases of SDLC);

• Practical knowledge of one of the following clouds: AWS, Azure, GCP in the area of Storage, Compute (+Serverless), Networking and DevOps supported by work on commercial projects;

• Familiarity with at least few of the following technologies: Cloud Dataflow, BigQuery, Cloud Dataproc, Cloud KMS, Pub/Sub, Google Kubernetes Engine, Apache Beam, Kafka, Spark, Snowflake, Data Lake Gen2, Event Hub, Data Factory, Databricks, Azure DWH, Azure Function, Power BI, Terraform, Redshift, Glue, Athena, Kinesis, Lambda;

• At least basic command of one of the programming languages: Python, Scala, Java, bash;

• Practical experience or strong interest in working with AI/ML solutions, including Generative AI (e.g. LLMs, prompt engineering, vector databases, RAG architectures);