Solution Architect - Generative AI

This is what we offer you

• Gross monthly salary between EUR 5,115 and EUR 7,308 (scale 10) for a 36 hour week
• Thirteenth month's salary and 8% holiday allowance
• 11% Employee Benefit Budget
• EUR 1,400 development budget per year
• Hybrid working: balance between home and office work (possible for most roles)
• A pension, for which you can set the maximum amount of your personal contribution

Are you passionate about creating cutting-edge AI solutions? Do you thrive in environments where you work closely with Data Scientists, Machine Learning Engineers, and Product Managers to design, build, and scale AI-driven innovation? If so, this role is for you.

You and your job

As a Solution Architect specializing in the application of GenAI, you will play a pivotal role in transforming AI prototypes into enterprise-grade solutions. You’ll define scalable architectures, establish best practices, and ensure the seamless integration of Generative AI models into production environments while fostering the organization’s growth in AI capabilities.

You will be part of the Tribe Data & Analytics Products and Transformation. The Tribe Data & Analytics Products and Transformation is empowering the Rabobank to maximize the value of data at scale. Every day the Squads of the Tribe work on innovative solutions to help the Rabobank reach this goal. The members of the Tribe continuously improve themselves and their Squads. We have an open culture and an urge to getting things done. Collaboration is at the heart of everything we do. All squads you interact with, both within the Tribe and outside, are working scrum/agile.

Practical examples

• Architecting Generative AI solutions involves designing various components of machine learning projects, focusing on Generative AI models, and defining interactions between them. Collaboration with data scientists is essential to select the most suitable tools, techniques, and technologies to craft these solutions. The goal is to develop architectures that balance performance, transparency, robustness, and scalability for Generative AI applications.
• Model management and lifecycle require robust model management by implementing appropriate tooling for versioning, monitoring, and maintenance throughout the model lifecycle. This includes designing release and retraining pipelines to ensure continuous model updates with minimal manual intervention, meeting organizational risk and compliance standards. Establishing feedback loops for real-time monitoring and issue resolution is also crucial, with a focus on low-effort maintenance.
• Data and process optimization involves setting requirements for input data to ensure data suitability and alignment with machine learning needs, preventing training/serving skew. Collaboration with platform teams is necessary to ensure a smooth integration of AI models into existing data ecosystems.
• Standardization and reusability are achieved by creating reusable components and standards to accelerate the development of new Generative AI solutions at scale. Aligning AI project designs with business processes and functions ensures seamless organizational integration.
• Roadmap and strategic alignment involve working with Product Managers, IT Leads, Architects, and Data Scientists to define roadmaps for Generative AI products, ensuring alignment with long-term business objectives. Staying informed on the latest trends in AI, such as synthetic data, transfer learning, and advanced model architectures, brings innovative insights into the organization.
• Operational excellence is defined by implementing CI/CD pipelines, unit testing, and statistical tests for efficient model deployment and maintenance. Setting up robust security designs for Generative AI solutions ensures compliance and involves discussing plans with engineering teams.
• Advocacy and leadership include promoting the use of Generative AI within the organization, providing thought leadership, and demonstrating its potential to solve complex business challenges. Gathering and sharing knowledge about scaling AI solutions effectively across large organizations is also important.

Together we achieve more than alone

We believe that bringing together people's differences is what makes us an even better bank. Talking of Rabobank: We are a Dutch bank that operates in 38 countries for over 9,5000,000 customers. Together with these customers, our members and partners we stand side by side to create a world in which everyone has access to enough healthy food. In the Netherlands we work to create a country in which people are happy with how they live, work and do business.

Within Rabobank, Analytics Acceleration is an ambitious department that aims to mix in a healthy dose of experience, innovation and creativity to develop innovative products and services. We focus on fast learning, delivering value quickly and being more effective. As a team, you work on clear goals and continuously see the results of your (team) efforts.

You and Your Talents

You embody open communication, a customer-focused mindset, and a structured way of working. You thrive on collaboration, feedback, and creating innovative solutions.

In addition to these talents, we are looking for someone with:

• Extensive Knowledge of Generative AI: 
• In-depth expertise in Generative AI solutions and familiarity with LLM models like GPT, DALL-E, including their limitations and strengths.
• Proven track record of implementing AI/ML solutions at scale, with experience in productionizing and maintaining models.
• Awareness of emerging trends in AI, including tooling for synthetic data, transfer learning, and more.

​Data Skills

• Familiarity with a wide range of database systems, including relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), graph databases (e.g., Neo4j), and time-series databases (e.g., InfluxDB).
• Ability to evaluate and recommend the most suitable database solutions for different AI applications, considering performance, scalability, and business needs.
• Proficiency in designing ETL (Extract, Transform, Load) systems that connect and process diverse data sources, such as structured, unstructured, streaming, and real-time data.
• Experience working with data pipelines and frameworks like Apache Airflow, Apache Spark, or similar tools to process large-scale data efficiently.
• A deep understanding of data preprocessing for Generative AI models, including data cleaning, augmentation, and synthetic data generation.
• Awareness of modern data architecture principles such as data lakes, lakehouses, and federated query systems, and how they intersect with Generative AI workflows.

Architecture and Cloud Proficiency:

• Knowledge of architectural concepts such as microservices, event-driven architectures, and domain-driven design.
• Experience with Azure services (Security, Networking, Azure AI services, ADF, Databricks, AzureML, Synapse, PowerBI) and Python development.
• Familiarity with the software development process, including CI/CD principles, package development, unit testing, quality assurance, and monitoring.

Consulting and Agile Experience:

• Strong consulting skills, with experience working in large-scale organizations.
• Proficiency in Agile/Scrum methodologies for effective team collaboration.

Tooling Knowledge:

• Able to draw architectural concepts using architecture diagramming tools like Draw.io, Visio, or ArchiMate.

You and the job application process

• Any questions about working at Rabobank and the process? Raphaël Drenthel, IT Recruiter via raphael.drenthel@rabobank.nl
• We will hold the interviews through a video call.
• A test can be part of the interview process.
• You can find answers to the most frequently asked questions on rabobank.jobs/nl/veelgestelde-vragen.
• A security check is part of the process.
• We respect your privacy.

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