Role – MLOps Lead Engineer
Location – Amersfoort, Netherlands (Onsite)
Mode - Contract
Experience - 12+ Years
Job Description:
Purpose of the Job:
- Responsible for delivering IT data science products and enabling all data science teams across organization to industrialize their data science models, from Commerce to Supply Chain.
- Responsible for run & maintain together with Managed Services, and provide support and change requests with the internal RFC team.
- Responsible for creating and maintaining Machine learning Operations (MLOps) framework and generic reusable components within the framework.
- This will shape the Machine Learning Operations capability within the D&A platform and enable teams beyond IT D&A. This will be the ‘toolkit’ used by all the data science and machine learning engineers across organization.
- Responsible for Solution design in the data science and Machine Learning Operations space. Enabling Innovation projects together with data science Capability Lead and Innovation Lead.
Business Complexity / Context:
- Owns the Data and Analytics (D&A) capability domain: drives principles, standards and best practices regarding the (Azure) data science platform across the capability, both within IT, as well as the data science community.
- Supports D&A Datascience team as Solution Architect in delivery of new Data science solutions
- Accountable for an Always On and Future Proof D&A data science platform
- Practice leader to guide and support Data Science & Machine Learning Engineers both in the global team, as well as in decentralized teams. Three to five direct reports (Machine Learning Operations Engineers) and indirectly influencing WoW from technology point of view of whole Data Science community across organization (~25-30 Data Scientists)
Areas of responsibility:
- Data & Analytics: Machine Learning Operations Capability (Data Science / Advanced Analytics / Innovation
Main Accountabilities /Key Tasks:
1. Scope of Work
- Responsible for delivering industry standard Machine learning Operations (MLOps) framework which will enable data scientists to build robust Machine Learning pipelines with speed and agility; enabling faster time to market: from exploration & prototyping to deployments in production. Secures buy-in from Senior Data & Analytics Functional Leaders (H19-H20) to implement and further scale the framework within their respective domains.
- Practice leader to guide and support data sxience & Machine Learning Engineers both in the global team, as well as in various business units
- Responsible for Quality Assurance as a reviewer of Machine Learning Engineering deliveries (pipelines, integrated frameworks), as well as executing performance evaluations of internal and external Machine Learning Engineers within direct team and in the periphery.
- Responsible for Machine Learning models in production and ensure that code pipelines (“CI/CD”) will run and act in case of breakdown.
2. Guidance
- Owns the Machine Learning Operations domain
- Responsible for driving tactical and operational execution of the Machine Learning Operations strategy within the Data and Analytics Cluster
- Pro-actively reports towards line manager and the Management team of the Data and Analytics Cluster
- Collaborates with Data Engineering, Data on progress
- Obtains decisions / approvals from line manager and/or D&A Domain Architect through agreed decision-making process (Architecture Review Board, and/or prepared decision brief)
3. Innovation & Solution Management
- Contributes to data science strategy and operationalizes this through creation of (innovative) solution architecture designs and execution plans.
- Leads first-time delivery of new ML solutions.
- Seeks continuous improvement and constantly challenges the status quo. Is very aware of new technologies and explores opportunities to leverage these to drive value.
- Drives innovation in the field of Data Science across the global FC D&A community and beyond its borders to enable a modern, agile digital landscape
4. Strategy & Planning
- Creates the vision and roadmap to develop the Machine Learning Operations domain within Global IT, Data and Analytics Cluster
- Collaborates with Data Engineering, Data Modeling, Data Integration and other relevant capabilities and partners to ensure that the Data & Analytics data science platform is performant, architecturally compliant and secure.
Critical Competencies:
Knowledge
- Masters Degree in Computer Science or similar and over 10 years of transferable experience in Data & Analytics (e.g. Platform Engineering / Data Engineering / Machine Learning Operations).
- Deep knowledge of (advanced) analytics and data science applications and latest development regarding implementation and productionizing.
- Experience and expertise in managing people, processes
- Experience in designing, managing, and implementing technology roadmaps
- Experience in facilitation of group session for product ideation and design.
- Proven experience with Azure (Data Platform) services and Azure Databricks (Delta Lake)
- Demonstratable experience in:
- Machine Learning Operations on Azure, Azure Infra and Security, Azure Logging and Monitoring, Airflow Scheduling, building and deploying with Azure DevOps
- Extensive experience with Azure Data Factory, Databricks using Python/PySpark, Azure Data Lake Storage, Azure Functions and Data modelling & SQL
- Setting up and maintaining CI/CD pipelines in Azure DevOps;
- Setting up data quality and monitoring;
- Databricks Lakehouse foundations
- Knowledge of idea management, brainstorming, design thinking, experimentation and innovation to lead the practical implementation of ideas
- Agile/DevOps way of working
Skills
- Problem ownership. Anticipate problems and how to defend against them at the right time and understand how the problem fits into the larger picture. Articulate the problem and help others to do it.
- Strategic ownership. Develop a long-term vision and objectives. User focus. Give direction on which tools or methods to use. Experienced in meeting the needs of users across a variety of channels.
- Change management. Initiate and champion change in product discovery, design and experimentation processes. Share ways of working and know how to make operations efficient. Competent in storytelling and influencing. Able to convince stakeholders both internally and externally.
- Innovation management. Oversee practical implementation of ideas. Anticipate and manage development risks. Organize for creativity in- and outside the cluster.
Attitudes
- Confident communicator
- Strong focus on delivery and are passionate about creating excellent products and services that meet user needs
- Your enthusiasm will help you collaborate with, and inspire an expert team
