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Own the technical target-state: You'll be the architect for Eneco's Data & AI platforms - designing one coherent, secure, compliant target state across Snowflake, Databricks, dbt, Airflow and Collibra. Deep technical architecture, real decision rights, zero pressure to also run the product backlog (that's owned by Platform Owners).  Our GenAI platform is evolving into an AI Product Control Plane and Agent Factory - you'll design the technical patterns, contracts and enforced gates that turn today's golden paths into reusable templates teams can ship agents on, safely and at scale.  Make governance and data-as-a-product first-class. Design domain-aligned data products and scaffolding for end-to-end governance where GDPR, EU AI Act and J-SOX controls are inherited by design, not bolted on - closing the gap from access-control tooling to real, lineage-aware governance.  Why choose Eneco? At Eneco, we're accelerating the energy transition through data and AI-driven decision-making at scale, guided by our One Planet plan and our ambition to become the first CO₂-neutral energy company by 2035. Our Data & GenAI Platforms are at the heart of this transformation, enabling product teams to deliver intelligent, secure, and compliant solutions that support the future of sustainable energy. As a Senior Architect – Data & AI Platforms, you will set the architectural vision and golden paths for Eneco’s data and AI ecosystem. You’ll work closely with Platform Engineering, Enterprise IT, and product teams to shape a scalable, interoperable, and future-proof platform landscape. In an environment that values collaboration, innovation, and continuous improvement, you will play a defining role in ensuring our platforms are trustworthy, cost-efficient, and enterprise-grade – empowering teams to innovate with confidence and speed. What you’ll do As a Senior Architect – Data & AI Platforms, you will own the architectural direction and evolution of Eneco’s Data & GenAI Platforms. You will define the target architecture and roadmap that enables high-velocity delivery of data, analytics, ML, and GenAI solutions while ensuring compliance, security, and efficiency. You will lead the design of a Lakehouse-centric ecosystem integrating technologies such as Databricks, Snowflake, Collibra, dbt, Airflow, and the Microsoft AI ecosystem (Copilot Studio, AI Foundry, Power Platform). A key focus will be building and scaling the GenAI platform, establishing standardized patterns for RAG, agent workflows, and LLMOps that enable product teams to rapidly deploy safe and reliable AI solutions. Through platform thinking and hands-on expertise, you’ll drive the industrialization of the modern data stack, embed governance by design, and improve developer experience through golden paths, templates, and reference architectures. You will act as a trusted advisor across the organization, coaching teams and contributing to Eneco’s strategic architecture vision. Is this about you? 10+ years of experience in data, analytics, or AI architecture, with at least 5 years leading modern cloud data platforms at scale. Deep expertise with Databricks, Snowflake, dbt, Airflow, and Collibra, including performance optimization, cost control, and governance design. Proven experience delivering GenAI and LLMOps platforms (RAG, vector search, prompt orchestration, evaluation frameworks, and guardrails), preferably on Azure. Strong knowledge of data governance, privacy and compliance (GDPR), lineage, metadata management, RBAC/ABAC, and policy-as-code. Proficiency with CI/CD and Infrastructure as Code (GitHub Actions, Azure DevOps, Terraform/Bicep) and platform SRE practices. Experience working across engineering, product, security, and enterprise IT stakeholders with strong communication and influencing skills. Deep understanding of architectural patterns such as Lakehouse, data mesh, event-driven design, CDC, and contract-first APIs. A pragmatic, solution-oriented mindset with a strategic vision for scalable, compliant platforms. Nice to have: Experience in the energy or utilities sector. Knowledge of Azure OpenAI integrations and enterprise search. Familiarity with NIS2 implications for critical infrastructure. Dutch language proficiency. You’ll be responsible for Defining and owning the target architecture and evolution roadmap for Data & GenAI Platforms, covering ingestion, transformation, orchestration, storage, serving, observability, and governance. Architecting and scaling a Lakehouse-based design interoperating with Databricks and Snowflake for analytics, ML, and serving use cases. Building and formalizing the GenAI ecosystem, including standardized RAG patterns, agent architectures, and LLMOps frameworks with strong safety and compliance guardrails. Establishing application patterns and “paved-road” templates that allow product teams to rapidly deploy AI environments and workflows. Industrializing the modern data stack with standardized dbt modeling practices and Airflow orchestration conventions. Implementing observability and FinOps monitoring for pipelines, models, cost control, quality, compliance, and performance. Operationalizing data governance through Collibra (or equivalent), embedding GDPR-compliant privacy-by-design and policy enforcement mechanisms. Ensuring interoperability with enterprise platforms, identity management, ITSM, and security standards in partnership with Platform Engineering and Enterprise IT. Leading the Architecture Community, sharing patterns, reviewing designs, mentoring teams, and driving adoption of best practices. Defining and tracking architecture success metrics such as reliability, time-to-value, cost efficiency, security, and reuse rates. This is where you’ll work You will join Eneco’s Data & AI domain, a central hub driving the company’s digital transformation and enabling smarter, sustainable energy solutions. Within this environment, you’ll collaborate closely with platform engineers, data engineers, AI specialists, product teams and enterprise IT to shape the future of Eneco’s data and AI capabilities. The culture is highly collaborative and ambitious, combining technical excellence with a strong sense of purpose: accelerating the energy transition. You’ll work in a hybrid setup from our Rotterdam office, where innovation, knowledge-sharing and continuous improvement are part of everyday practice — giving you the space to influence strategy, architecture and real-world impact at scale.

Build production-grade machine learning solutions using Databricks, MLflow, AWS and modern MLOps practices. Own the complete machine learning lifecycle, from scalable data processing and model training to production deployment and monitoring. Collaborate in a multidisciplinary engineering team where Machine Learning Engineers, Data Scientists and Data Engineers build reliable, scalable AI solutions together. Why choose Eneco? At Eneco, we are committed to accelerating the energy transition. Through our One Planet strategy, we aim to become climate neutral by 2035 – together with our customers. Digital technology and data play a key role in achieving this ambition. Within the Energy Management Systems domain, we develop reliable, scalable and intelligent data-driven solutions that support Eneco's digital products and services. As a Machine Learning Engineer, you will help bring machine learning solutions into production by building the platforms, tooling and engineering practices that enable models to deliver reliable business value. Working closely with Data Scientists, Data Engineers and Software Engineers, you will build and operate production-grade machine learning solutions while helping shape the future of MLOps within Eneco. What you’ll do Design, build and maintain end-to-end machine learning pipelines for training, validation and production inference in both batch and real-time environments. Develop scalable MLOps capabilities, including CI/CD pipelines, automated testing, model versioning, deployment strategies, drift detection and rollback mechanisms. Build and operate Databricks-based data processing and machine learning workflows at scale. Manage MLflow for experiment tracking, model registry and reproducible machine learning deployments. Deploy, operate and optimize machine learning workloads on AWS while balancing performance, scalability and cost. Develop scalable feature engineering pipelines, data contracts and feature stores, including Scala-based ETL pipelines where applicable. Build monitoring, alerting and observability for production models, including model performance, latency, throughput, and data and model drift. Work closely with Data Scientists to productionize machine learning models, provide feedback on production constraints and continuously improve deployed solutions. Perform post-deployment analyses to evaluate and improve model performance and operational reliability. Contribute to platform improvements, automation and MLOps best practices across the team. Mentor fellow engineers and contribute to a strong engineering culture. Is this about you? You are passionate about building reliable production systems and enjoy bringing machine learning solutions into production. You combine strong software engineering skills with hands-on MLOps experience and thrive in multidisciplinary teams where engineering and data science come together. 4+ years professional experience in ML engineering; 2+ years owning MLOps/production ML systems. Hands-on experience with Databricks (notebooks, jobs, clusters) for large-scale data processing and model training. Production experience with MLflow for experiment tracking and model registry. Strong AWS experience deploying and operating ML workloads. Proficient in Scala for data processing/ETL on Spark; production-quality code and familiarity with JVM ecosystem. Experience building CI/CD for ML and containerized deployments (Docker, Kubernetes). Practical knowledge of model monitoring, A/B testing, canary deployments, and data/model drift detection. Strong software engineering fundamentals: testing, design patterns, code reviews, and documentation. Excellent communication and collaboration skills. You’ll be responsible for Build, deploy, and maintain end-to-end ML pipelines for training, validation, and production inference (batch and real-time) Design and implement robust MLOps practices: CI/CD for models, model versioning, automated testing, drift detection, and rollback strategies. Implement and operate Databricks-based data processing and model training workflows at scale. Integrate and manage MLflow for experiment tracking, model registry, and reproducible runs. Design, deploy, and optimize ML workloads on AWS with cost and performance tradeoffs. Develop and maintain data contracts, feature stores, and scalable feature engineering pipelines (including Scala-based ETL where applicable). Build monitoring, alerting, and observability for model performance, latency, throughput, and data/label drift. Collaborate with data scientists to productionize models, provide feedback on model design for production constraints, and run post-deployment analyses. Mentor engineers on MLOps best practices and contribute to team-wide tooling and automation. This is where you’ll work You will join a multidisciplinary team of Machine Learning Engineers, Data Scientists, Data Engineers and Data Analysts within the Energy Management Systems domain. Together, you develop, deploy and operate machine learning solutions that support Eneco's digital products and services. Our team values collaboration, ownership and continuous learning. We encourage experimentation, knowledge sharing and continuous improvement while building reliable, scalable machine learning solutions that contribute to Eneco's ambition to accelerate the energy transition. What we have to offer Gross annual salary between €84.000 and €117.000 Including FlexBudget, 8% holiday allowance, and depending on your role a bonus or collective profit sharing. FlexBudget Including FlexBudget, 8% holiday allowance, and depending on your role a bonus or collective profit sharing. Have it paid out, use it to buy extra holiday days or save it up for something nice, it's up to you. Personal and professional growth Eneco is fully committed to help you in your personal and professional development. Hybrid working: home, office or abroad Work 40% at the office, 40% from home, and 20% flexibly. With manager approval, you may work abroad (within approved countries) up to 3 weeks/year, max 2 consecutively. Want more information about our terms of employment? Here's what you get from us Work Where Everyone Matters When you choose a career at Eneco, you choose ambition, growth, and opportunity in an environment where everyone matters. You’re given the space to develop yourself and to do your work in a way that suits you. We believe that different perspectives, nationalities, and backgrounds make us stronger, which is why we foster an open, safe and inclusive culture. Naturally, we also prioritize a healthy work-life balance, flexible working hours, and the option to work from home when your role allows it. If you have a physical or sensory disability, we will work with you to find the right adjustments so you can perform your job well. This is how you build your own future and a sustainable future at the same time. Together with 4,000 colleagues, each with their own talents and ideas, you work on our shared mission: speeding up the energy transition. We help customers become more sustainable faster, create innovative solutions, and seize new opportunities. Will you join us? The phases of our application procedure Want to know more about this job function? Contact our recuiter at: [email protected]