Forward Deployed Engineer (AI Ops)

Join VTTI’s Applied AI Team to Shape the Future of Energy

VTTI is on the hunt for a Forward Deployed Engineer to join our newly formed Applied AI Team! In this pivotal role, you will help develop AI Products that transform our 15+ global storage terminals into intelligent and adaptive ecosystems. Your expertise will span machine learning, model deployment, AI systems architecture, and scalable infrastructure. You won’t just build models; you’ll engineer the full lifecycle of AI systems, drive strategic AI initiatives, and act as a technical leader for the organization. If you’re ready to be a key part of our purpose, “Energy to Move Tomorrow,” and drive innovation in energy storage and sustainability, we want to hear from you.

Job Description

With a central Data & AI team based in Rotterdam and the creation of our Applied AI Team, we are expanding our capabilities to include product-focused AI initiatives. You will be part of a cross-functional team dedicated to developing and delivering impactful AI Products. These include applications powered by Generative AI, Retrieval-Augmented Generation (RAG), and multi-cloud deployments. Your mission: to design, implement, and maintain scalable AI solutions that provide actionable insights and improve operational efficiency.

Responsibilities

Strategy & Architecture

• Design, deploy, and maintain robust AI/ML infrastructure and pipelines.
• Integrate advanced AI capabilities, including Generative AI, RAG, MCP (Model Context Protocol) and predictive modeling into business workflows.
• Implement scalable solutions across Multi-Cloud Platforms, ensuring performance, resilience, and compliance.
• Monitor deployed models for performance degradation, bias, and data drift, enabling proactive maintenance.

AI System Engineering

• Build and maintain production-grade AI systems using Python and ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
• Develop robust CI/CD pipelines for ML using tools such as MLflow, Azure DevOps and GitHub Actions.
• Manage the end-to-end lifecycle of ML models: data preprocessing, model training, validation, deployment, and monitoring.
• Ensure reproducibility, auditability, and security in all AI/ML solutions.

Cross-Functional Collaboration

• Work closely with Data Scientists, AI Champions and business stakeholders to define and execute AI Product Roadmap.
• Translate business needs into scalable, maintainable AI solutions that generate measurable impact.
• Guide and upskill engineers and analysts across teams in AI best practices.

Data Integration & Compliance

• Enable seamless data access and transformation through integration with structured and unstructured data sources.
• Ensure data governance, security, and compliance across AI workflows.
• Support the integration of AI services with platforms like Azure Databricks, Denodo, Azure OpenAI Services, and Azure Data Fabric.

Competencies

AI Engineering & MLOps Expertise

• You build and manage machine learning pipelines and production systems that are robust, automated, and observable.
• Experience with Retrieval-Augmented Generation, vector databases (e.g., FAISS, Pinecone), and LLM integration.
• Proficiency in containerization (Docker) and orchestration (Kubernetes) for deploying AI solutions at scale.

Multi-Cloud Architecture

• Knowledge of deploying AI services across multi-cloud environments (Azure, AWS, GCP).
• Familiarity with IaC tools like Terraform for scalable infrastructure provisioning.

Programming & Tools

• Strong programming skills in Python (must-have), and familiarity with SQL, Java is a plus.
• Hands-on experience with tools such as MLflow, TFX, Airflow, Spark, or similar.

Leadership & Strategy

• Drive AI adoption across teams by fostering a collaborative and learning-centric environment.
• Contribute to the strategic roadmap of AI capabilities across the organization.

Requirements

• A degree or higher in Computer Science, Artificial Intelligence, Data Science, or a related technical discipline.
• Minimum 4 years of experience in AI/ML Engineering, with demonstrated success deploying production AI systems.
• Hands-on experience with building and scaling MLOps infrastructure and CI/CD pipelines.
• Proficiency in cloud-native AI/ML deployment across Azure, AWS, and/or GCP.
• Deep understanding of modern AI techniques including RAG, LLM fine-tuning, vector search, and prompt engineering.
• Strong foundation in data engineering concepts, data modeling, and pipeline orchestration.

Join us in the Applied AI Team and help shape the future of energy through responsible, cutting-edge AI innovation. Deliver on our promise to provide essential energy with intelligence and purpose. This position is also open to candidates located in Singapore or the UK.