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Machine Learning Engineer
hace 3 semanas
Machine Learning Engineer
Location: Barcelona, Hybrid Working (2 days onsite, 3 days remote)
Start Date: ASAP
End Date: December 2025
Contract type: B2B freelancing
About the Role:Our client is seeking a highly skilled and experienced Machine Learning Engineer to join a project in Barcelona. This is a hybrid role with the flexibility of working two days onsite and three days remotely.
Key Responsibilities:- Develop and deploy machine learning systems into production.
- Work with a variety of relational SQL and NoSQL databases.
- Utilize big data tools such as Hadoop, Spark, and Kafka.
- Develop serverless code with at least one cloud provider solution (AWS, GCP, Azure).
- Program in object-oriented/object function scripting languages like Python, Java, C++, Scala.
- Develop predictive models in a production environment and integrate them into larger applications (MLOps).
- Use Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow, or PyTorch.
- Leverage GenAI and multimodal AI knowledge via HuggingFace, Llama, VertexAI, AWS Bedrock, or GPT.
- Manage data pipelines and workflows with relevant tools.
- Follow standard software engineering methodologies, including unit testing, test automation, continuous integration, code reviews, and design documentation.
- Work with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, TFX.
- Experience with Docker and Kubernetes is a big plus.
- University or advanced degree in engineering, computer science, mathematics, or a related field.
- 7+ years of experience developing and deploying machine learning systems into production.
- Experience with a variety of relational SQL and NoSQL databases.
- Experience with big data tools: Hadoop, Spark, Kafka.
- Experience with at least one cloud provider solution (AWS, GCP, Azure) and understanding of serverless code development.
- Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala.
- Previous experience developing predictive models in a production environment, MLOps, and model integration into larger-scale applications.
- Experience with Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow, or PyTorch.
- Exposure to GenAI and solid understanding of multimodal AI via HuggingFace, Llama, VertexAI, AWS Bedrock, or GPT.
- Knowledge of data pipeline and workflow management tools.
- Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation.
- Working experience with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, TFX.
- Relevant working experience with Docker and Kubernetes is a big plus.
If you are passionate about machine learning and meet the qualifications, we would love to hear from you. Apply now or email me to discuss further.
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