AI/ML Engineer Summary Location:
Barcelona (Hybrid) OR Madrid (Hybrid) OR Reading, UK (Hybrid) Day Rate:
Negotiable Duration:
6 Months (with a potential view to extend further) Availability:
ASAP
About the Client My client is the air transport industry's IT provider, delivering solutions for airlines, airports, aircraft, and governments. Their technology powers more seamless, safe, and sustainable air travel. They are looking to hire an experienced AI/Machine Learning Engineer to support projects focused on software development and AI topics.
About the Role Job Summary: A general technology organisation operating in the aviation ecosystem are seeking an experienced AI/ML Engineer to support a portfolio of high-impact artificial intelligence initiatives. This is a 6-month contract based in Barcelona, Madrid or Reading (UK), working within a modern engineering environment focused on delivering scalable, production-grade AI solutions.
Key Responsibilities: • Design, develop, and deploy machine learning models and AI components for enterprise-level applications. • Build robust, scalable infrastructure for training, experimentation, and model serving using Python, Kubernetes, and related cloud-native technologies. • Collaborate closely with software engineering, data engineering, and product teams to integrate ML solutions into broader platform architectures. • Drive best practices for MLOps, including CI/CD pipelines, containerisation, monitoring, and optimisation of ML workloads. • Conduct data pre-processing, feature engineering, and model evaluation using modern ML frameworks. • Document architecture, workflows, and technical decisions to support cross functional transparency and operational excellence.
What we are looking for Required Skills & Experience: • Strong professional experience as an AI/ML Engineer, Machine Learning Engineer, or similar advanced technical role. • Proficiency in Python and major ML/AI libraries (e.g., TensorFlow, PyTorch, scikitlearn). • Hands-on expertise with Kubernetes, containerisation (Docker), and cloud-native deployment patterns. • Experience building and maintaining production-quality ML pipelines and modelserving environments. • Familiarity with modern MLOps tooling (e.g., MLflow, Kubeflow, Airflow, Argo). • Strong understanding of software engineering principles, version control, and DevOps workflows. • Excellent communication and cross-team collaboration capabilities.
Nice to Have: • Experience with cloud platforms (AWS, Azure, or GCP). • Background or exposure to aviation, transport, or mission-critical technology environments. • Understanding of distributed systems, real-time data processing, or microservices architectures.