AI Engineer

About the Client

Our client holds a prominent position in the European utilities sector, dedicated to seamlessly connecting people and businesses with energy and providing customer-centric solutions designed for sustainability.

The company develops cloud-based, state-of-the-art software applications in various areas - IoT/ IIoT, energy management & trading, predictive maintenance.

The Role

Responsibilities

Partner with business stakeholders to analyze challenges, gather requirements, and shape AI-driven solution strategies.
Design and implement scalable AI applications using Generative AI, large language models, agentic workflows, classical machine learning techniques, and time series forecasting where appropriate.
Lead end-to-end delivery of AI initiatives, including problem definition, architecture design, development, deployment, monitoring, and optimization.
Build and maintain AI solutions on cloud platforms such as Azure and AWS.
Define and promote engineering best practices covering architecture, code quality, testing, CI/CD, MLOps, LLMOps, security, observability, and responsible AI.
Collaborate with cross-functional teams to ensure reliable, maintainable, and scalable enterprise AI solutions.
Mentor team members, share technical expertise, and support capability development across the organization.

Must-haves

MSc, PhD, or equivalent academic background in Computer Science or a related discipline.
Extensive experience as an AI Engineer, Machine Learning Engineer, or Data Scientist with a strong focus on AI solutions.
Hands-on expertise in Generative AI, LLM-powered applications, Retrieval-Augmented Generation (RAG), classification, and regression models.
Strong software engineering background with advanced Python development skills.
Experience with AI and machine learning frameworks including scikit-learn, XGBoost, TensorFlow, PyTorch, LangChain, and LangGraph.
Practical experience with agent-based systems and agentic architectures.
Proven success delivering AI and machine learning solutions from proof of concept through production deployment.
Experience building and operating solutions on Azure and/or AWS cloud platforms.
Solid understanding of software architecture, system integration patterns, deployment approaches, security principles, and enterprise-scale solution design.
Knowledge of CI/CD practices, monitoring, observability, MLOps, and LLMOps.
Excellent stakeholder management, communication, and workshop facilitation skills.

Nice-to-haves

Experience working with Databricks.
Previous experience operating in a consulting or professional services environment.

Benefits

Annual performance bonus
25 days of holiday
Medical Insurance
Many other perks

Selection Process

One informational interview with a local hiring manager
One technical interview with a team leader based on your experience with machine learning frameworks, agent-based systems, public cloud platforms and DevOps practices

Good to Know

The client values engineers who think like consultants. They expect you to be adaptable, proactive, and comfortable wearing multiple hats.
You may be asked to engage directly with business stakeholders to gather requirements and understand the bigger picture of the project you are involved in.