Senior Ai/ml Engineer, Ml Platform Ai Coe

**Location**

Global (Based in Barcelona, Spain)

**Division**

Industrial Print SW & Solutions

Your mission at HP Industrial Print

Are you ready to shape the future of the printing industry? HP Industrial Print, a division of HP Inc., is at the forefront of providing innovative printing solutions and services for the graphic arts and commercial printing sectors. We're driving the digital transformation of the print industry with a focus on accessibility, agility, security, and sustainability.

Our groundbreaking products and services enable businesses to stay ahead of market changes and expand their capabilities with world-class digital printing solutions. With a vision to revolutionize Industrial Print, we've deployed more than 8000 presses worldwide, serving 5500 customers across 82 countries.

As leaders in the commercial, label, and packaging segments, HP Industrial Print is dedicated to empowering businesses to unlock their full potential, driving limitless growth and new opportunities.

At the heart of HP’s business strategy is Sustainable Impact across three pillars of Climate Action, Human Rights, and Digital Equity. Rooted in science. HP is recognized as one of the world’s most sustainable companies. It was the only tech company globally to receive 2023 Triple “A” rating from CDP for its’ disclosure and progress within Climate, Water, and Forests for the 5th year in a row, among many other recognitions.

We're looking for you

We are searching for a dedicated and talented Machine Learning Engineer who is excited about building and maintaining robust machine learning infrastructures and deploying models at scale. Your expertise in automating ML workflows, managing model lifecycles, and ensuring the seamless deployment of AI solutions will be crucial in driving our projects forward.

If you have a strong background in MLOps-related technologies, and a deep understanding of machine learning platforms and pipelines, we would love to hear from you.

**Key Responsibilities**
- Develop and implement automated pipelines for ML model deployment and retraining.
- Collaborate with data scientists, software engineers, and product managers to integrate and deploy ML models into production.
- Collaborate with the DataOps and DevOps teams in designing, building, and maintaining scalable and reliable machine learning infrastructure.
- Manage model versioning, experiment tracking, and model monitoring to ensure reproducibility and performance.
- Monitor the performance of ML models in production, identify model drift, and automate model retraining processes.
- Develop and manage internal libraries to accelerate ML model development and standardize best practices.
- Collaborate with the Data Architects and the DevOps team to develop and manage our feature store to streamline feature engineering, sharing, and reuse across models.
- Implement data pipelines to ensure the availability and quality of data for training and inference.
- Stay updated with the latest developments in MLOps tools, cloud platforms, and model serving technologies, incorporating them into our workflows.
- Troubleshoot and debug issues related to model deployment, scaling, and infrastructure in collaboration with cross-functional teams.
- Document ML processes, infrastructure setup, and best practices to ensure smooth operations and knowledge sharing.
- Ensure the security, reliability, and ethical deployment of AI models in production environments.

**Qualifications**
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- 5+ years of experience in deploying and managing machine learning models in production environments.
- Proficiency in programming languages such as Python is required.
- Strong knowledge of MLOps frameworks and libraries (e.g., MLflow, Kubeflow, TensorFlow Serving).
- Experience with cloud platforms like AWS, Azure, or Google Cloud, including services for model deployment and orchestration (e.g., SageMaker, Vertex AI).
- Understanding of CI/CD practices, containerization (Docker), and orchestration (Kubernetes).
- Strong problem-solving skills and the ability to automate workflows for efficient ML deployment.
- Excellent communication skills to convey technical concepts to both technical and non-technical stakeholders.
- Ability to work collaboratively in a team environment.
- Commitment to continuous learning and staying updated with the latest MLOps advancements.
- Familiarity with ethical considerations and best practices in deploying AI solutions.
- Relevant certifications in MLOps, cloud platforms, or DevOps are a plus.
- Fluency in English (equivalent to C1 or higher).

Sales & Services entity (ES11)