Data and MLOps Engineer

Our requirements

• Bachelor’s or master’s degree in computer science/engineering, data engineering, data science, or a related field.
• Strong programming skills in Python; experience with additional programming languages (e.g., Java, MATLAB, etc.) is considered an asset.
• Solid understanding of database technologies, including SQL, query optimization, schema design, and complex joins; familiarity with NoSQL and time-series databases is considered an asset.
• Experience building and maintaining data pipelines and workflows using modern data platforms such as Databricks, including technologies such as Unity Catalog, Delta Live Tables, MLflow or similar tools.
• Hands-on experience developing and deploying solutions on cloud platforms such as Azure, GCP, or AWS; relevant cloud certifications (particularly Azure or GCP) are considered a strong asset.
• Experience with CI/CD pipelines and DevOps practices, including tools such as Azure DevOps, GitHub Actions, Git, or similar technologies.
• Experience developing APIs and containerized applications, including RESTful services and technologies such as Docker and Kubernetes.
• Strong collaboration and communication skills, with the ability to work effectively across multidisciplinary teams and quickly learn and apply new technologies and concepts.
• The following experience and skills are considered strong assets:
• Familiarity with industrial IT/OT environments, including industrial data historians (e.g., AVEVA PI System), industrial protocols (e.g., OPC UA, MQTT), and real-time data integration architectures.
• Experience developing or operationalizing LLM and Generative AI workflows, including retrieval-augmented generation (RAG) use cases.
• Experience with modern frontend technologies (e.g., React) for building data-driven web applications and user interfaces.
• English at C1 level

Optional

• Spanish language

Your responsibilities

• Solution Development:
• Learn, understand, and leverage Hatch and/or clients’ existing data infrastructure to design and implement scalable, secure, and high-performance data solutions within industrial IT/OT environments, supporting AI/ML, optimization, and analytics applications.
• Build, configure, and maintain real-time and batch data pipelines to ingest, process, and transform industrial datasets, including time-series process data, sensor data, and operational data from diverse sources.
• Collaborate closely with software developers, data scientists, and domain experts to operationalize AI, machine learning, and optimization models into scalable, maintainable, and production-grade systems deployed in cloud and/or edge environments.
• Design, implement, and maintain MLOps workflows and infrastructure to support reliable model training, automated deployment, monitoring, lifecycle management, and version control for data, models, and pipelines.
• Industrial Systems Integration:
• Collaborate with process engineers, control specialists, software teams, and client IT/OT stakeholders to implement seamless integration across plant systems and enterprise applications, including data historians, control systems, edge computing environments, etc.
• Ensure alignment with cybersecurity standards and network architecture best practices (e.g., DMZ architecture, secure APIs, and secure communication protocols).
• Delivery Excellence and Technology Development:
• Understand client business objectives, operational requirements, and technical constraints to support the delivery of practical and scalable solutions.
• Support rapid proof-of-value initiatives, pilot deployments, and solution implementation activities, with consideration for transitioning prototypes into scalable and sustainable production environments.
• Contribute to technical documentation and the development of reusable assets, including standardized data pipelines, MLOps and DevOps practices, edge AI/optimization deployment frameworks, and implementation accelerators.
• Stay current with emerging technologies and industry trends in cloud and edge computing, distributed systems, AI/ML platforms, data engineering, and industrial digital transformation.

What we offer

• Flexible work environment
• Long term career development
• Think globally, work locally

Benefits

• sharing the costs of sports activities
• private medical care
• sharing the costs of professional training & courses
• life insurance
• flexible working time
• fruits