Senior Machine Learning Engineer (MLOps)

Dow Jones; Barcelona; Presencial; EUR 70.000 - 110.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Parental leave 20 weeks

Caregiver leave 4 weeks

Remote work up to 4 weeks/yr

Volunteer day

Caregiver reimbursement $700

Headspace wellness membership

ClassPass fitness/wellness

Descripción de la vacante

Dow Jones in Barcelona, Spain seeks a Senior Machine Learning Engineer to own the full MLOps lifecycle, with a strong focus on LLMOps, from development to deployment.

You will collaborate with data scientists and product teams to design scalable pipelines, implement monitoring, retraining, and CI/CD practices for production ML. Proficiency in Python/SQL, cloud platforms, and LLMs is required.

Formación

• 5+ years of experience in ML/AI and MLOps.
• Strong hands-on experience with LLMOps, fine-tuning, and prompts.
• Experience with production ML systems and monitoring.

Responsabilidades

• Own end-to-end ML model development, optimization, and deployment.
• Design and maintain scalable ML pipelines and CI/CD for ML systems.
• Develop and evaluate LLM-based solutions and multi-agent systems.
• Collaborate with data scientists, engineers and product teams to integrate ML solutions.

Conocimientos

LLMOps

Python

SQL

AWS

GCP

Azure

PyTorch

TensorFlow

Scikit-learn

MLflow

Airflow

Pandas

NumPy

Agent systems

Educación

Bachelor’s degree in CS/Stats/Math

Master’s degree preferred

PhD strong plus

Herramientas

Snowflake

Spark

Dask

MLflow

CI/CD tooling

Descripción del empleo

• In this pivotal role, you will own the entire lifecycle of MLOps from optimization to deployment, with a strong focus on LLMOps
• We are seeking a highly experienced and technically adept Senior Machine Learning
• Engineer to join our team. In this pivotal role, you will own the entire lifecycle of MLOps
• From optimization to deployment, with a strong focus on LLMOps. This position
• Demands a strong blend of hands-on technical expertise, strategic thinking, and the
• Ability to foster innovation within a dynamic environment
• You Will:
• Develop and manage LLM-based solutions, including semantic layer
• Development, fine-tuning, model evaluation, deployment strategies, and the
• Development of agent and multi-agent systems
• Own end-to-end ML model development, optimization, and deployment
• Ensure the scalability, efficiency, and reliability of ML pipelines
• Design and implement robust model monitoring and retraining strategies
• Optimize model inference and performance for production environments
• Collaborate closely with data scientists, engineers, and product teams to
• Integrate ML solutions
• Improve experimentation frameworks, model versioning, and A/B testing
• Strategies
• Ensure best practices in MLOps, including automation, reproducibility, and CI/CD
• For ML
• Contribute to architectural decisions and improve ML infrastructure
• Mentor and provide technical guidance to junior ML engineers

Benefits

• 20 weeks of parental leave
• 4 weeks of paid caregiver leave
• Up to 4 weeks of remote work per year
• 1 paid volunteer day annually
• $700 caregiver reimbursement
• Headspace emotional wellness membership
• Financial rewards for well-being activities
• ClassPass fitness and wellness membership

We are seeking a highly experienced and technically adept Senior Machine Learning Engineer to join our teamThis position Demands a strong blend of hands-on technical expertise, strategic thinking, and theSnowflake experience strongly preferredStrong experience with LLMOps, including fine-tuning frameworks, promptProficiency in Python and SQL and deep, hands-on experience with MLQuantitative field; a Master’s degree or Ph.D. is a strong plusA grasp of the broader data science toolkit, including libraries like Scikit-learn,Frameworks like TensorFlow, PyTorchExperience with cloud platforms (AWS, GCP, or Azure) and large-scale dataA Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related2-4 years of professional experience in machine learning with a strong portfolioExperience in the development of agent and multi-agent systemsPandas, and NumPyOf models deployed in a production environmentAdvanced hands-on experience with cloud-based data warehouse solutionsProcessing frameworks like Spark or Dask is preferredAirflowExperience building and maintaining MLOps pipelines using tools like MLflow, orEngineering, and managing the lifecycle of large language models, as well as