AI Engineer 2 (Data & AI Department)

​(1)   AI Solution Development: Work collaboratively with Data Scientists and Data Engineers to design, test, and implement AI/ML models for EMA's diverse use cases across the complete AI/ML lifecycle. Lead the development of production-ready AI solutions that can be deployed reliably, timely, and consistently to support critical energy sector operations and regulatory functions.

(2)   MLOps and Production Lifecycle Management: Establish and maintain comprehensive MLOps foundations covering the end-to-end journey from code commit to model retirement. Design continuous integration, continuous deployment, and continuous training (CI/CD/CT) pipelines whilst managing version control across code, data, and models. Implement automated retraining infrastructure with performance monitoring, data drift detection, and guardrail systems to ensure models remain safe and effective in critical infrastructure environments.

(3)   Stakeholder Management and Technical Leadership: Lead engagement and alignment initiatives with senior management, data scientists, business users, and vendor/IT teams. Translate complex AI outputs into actionable insights and business intelligence for non-technical audiences. Bridge communications between diverse stakeholders to identify optimal trade-offs between business outcomes, system performance limitations, and compliance requirements.

• Higher Tietiary background in Computer Science, Mathematics, Physics, Data Science or related quantitative field. Specialised AI certifications advantageous

• Minimum 5-10 years of professional experience developing, deploying, and maintaining enterprise-grade AI/ML models in production environments

• Demonstrable experience across the full MLOps lifecycle

• Expert proficiency in AI/ML modelling including supervised/unsupervised learning, deep learning, and reinforcement learning

• Advanced programming skills in Python and frameworks such as PyTorch, TensorFlow, Pandas, and equivalent technologies

• Comprehensive knowledge of MLOps tools and practices for automated model training, testing, deployment, and monitoring

• Experience with model optimisation techniques including quantisation, pruning, and knowledge distillation

• Exceptional stakeholder management abilities with proven track record of translating technical concepts for diverse audiences

• Systems thinking approach to problem-solving within complex technical constraints

• Independent professional with excellent interpersonal and communication skills

• Innovative mindset with ability to identify optimal technical trade-offs for decision-making​