Role:Senior AI/ML Data Architect

Location: West Midlands

Turner Lovell are recruitinga Senior AI/ML Data Architectwith a strong background in developing and deploying artificial intelligence (AI) and machine learning (ML) solutions.

The role involves developing grid automation applications, collaborating with teams to identify data-driven opportunities, and leading AI/ML model development to optimise power systems.

You will ensure efficiency, scalability, and security in applications, leveraging your MLOps and full ML lifecycle skills.

Key Responsibilities:

• Lead the design and implementation of AI/ML applications to deliver differentiated products and solutions in the grid automation sector.

• Define and implement the framework for structuring and leveraging databases for AI to extract actionable insights.

• Develop, deploy, and optimise high-quality AI/ML models on cloud-native or on-premise platforms, using container or microservices architecture.

• Work closely with R&D, product management, and regional teams to understand needs and develop innovative solutions.

• Oversee the end-to-end lifecycle of ML models, including data pipeline development, training, deployment, and optimisation.

• Identify areas where AI/ML can be applied to drive business efficiency, enhance customer satisfaction, and solve critical customer problems.

• Collaborate with internal teams to ensure IP clearance and manage AI/ML model performance in production.

• Lead and mentor teams, ensuring alignment with strategic objectives and best practices in AI/ML development.

Requirements:

• Relevant degree in Computer Science, Electrical Engineering, Information Technology, or a related field, with at least 5 years of experience in data science, AI/ML, or power systems.

• Proven experience in applying AI/ML techniques, including supervised/unsupervised learning and reinforcement learning, in industrial or OT (Operational Technology) environments.

• Hands-on experience with AI/ML frameworks, MLOps (CI/CD), and cloud-native deployment in environments like Azure, AWS, or Google Cloud.

• Experience with grid power system simulations, MATLAB, PSCAD, PSS/E, Digsilent, or similar tools for power system modelling.

• Strong background in developing and deploying AI/ML models in predictive maintenance, load forecasting, and grid optimisation.

• Proficiency in programming languages such as Python, C++, Java, R, or similar.

• Expertise in deploying ML models using Docker, Kubernetes (K8s), and related tools in virtualised environments.

• Experience with data pipelines, Azure ML, SQL/NoSQL databases, and real-time data distribution systems.

• Excellent problem-solving skills with a strong ability to troubleshoot, debug, and optimise AI/ML applications.

• Proven ability to work collaboratively in cross-functional teams, leading projects from concept to implementation.

• Strong communication and organisational skills, with the ability to lead, mentor, and provide guidance to other team members.

Desired Experience:

• 3+ years of experience in developing ML models for power systems, including predictive analytics and optimisation techniques.

• Experience with distributed computing using Spark, Scala, and cloud environments.

• Familiarity with GPU computing for large-scale ML model training.

• Advanced experience with databases such as GraphDB, SQL/NoSQL, and MS Access.

• Knowledge of modern protection, control, and automation technologies in power distribution systems.

• Experience with presenting technical papers at industry conferences or publishing in academic journals.

• A proactive and adaptable approach, with a passion for continuous improvement and driving innovation.

Our client offers exciting opportunities to work on cutting-edge technologies in the energy sector. With a focus on sustainability and innovation, this is a chance to contribute to ground-breaking projects that shape the future of grid automation and power systems.

If this sounds like it could be your next challenge, please apply or contact Yana Arif () / for further information.