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# ML Engineer

- **Company:** adesso SE
- **Location:** Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, ARM Architecture, Big Data, BigQuery, Cloud Computing, Github, Identity and Access Management, Machine Learning, Search Technologies, Google Cloud, Snowflake, Prompt Engineering, Build Server, Build Management, Google Cloud Functions, Machine Learning Operations, Multiaccess Edge Computing, Terraform
- **Published:** September 23, 2026
- **Apply:** https://jobs.adesso-group.com/talentcommunity/apply/1369304455/?locale=en_GB

## About the Role

* Hands-on experience with GCP services: Model Garden, Evaluation, Batch Inference, BigQuery (including Vector Search), Vertex AI Pipelines, Cloud Build, and Cloud Run
 * Strong understanding of ML concepts around product similarity, Transformer models, embeddings, and kNN
 * Experience with prompt engineering and Cortex AI on Snowflake

Nice to Have

 * Experience with Airflow and dbt
 * Familiarity with Terraform and data modelling
 * Working knowledge of cloud infrastructure, especially Cloud Build, Cloud Run, and IAM
 * Experience with GitHub Actions

## Description

Are you passionate about building intelligent, scalable ML systems on the cloud? We're looking for a Senior ML Engineer to join our team and work on cutting-edge machine learning solutions using Google Cloud Platform and Snowflake's Cortex AI. You'll be part of a team that designs, deploys, and operationalizes ML models that power real business outcomes for our enterprise clients.

What You'll Do

 * Build and deploy ML solutions using GCP's Agent Platform - including Model Garden, Evaluation, and Batch Inference (Batch Prediction jobs)
 * Work with BigQuery and BigQuery Vector Search to power large-scale data and similarity search use cases
 * Design and manage ML pipelines using Vertex AI Pipelines (MLOps), Cloud Build, and Cloud Run jobs
 * Develop solutions involving product similarity, Transformer models, embeddings, and kNN-based approaches
 * Apply prompt engineering techniques and leverage Cortex AI features within Snowflake
 * Collaborate with cross-functional teams to translate business problems into production-ready ML systems


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