Cloud Engineering Specialist

About the role

You will establish and drive the standards, frameworks, and best practices that underpin the Site Reliability Engineering (SRE) function. Working collaboratively across multiple technology teams and closely with the Service Assurance organization, you will ensure the consistent adoption of reliability, observability, and operational excellence disciplines while fostering a culture of continuous improvement and cross-functional collaboration. You will actively contribute to the design, development, and validation of Generative AI models within the AWS cloud environment, creating scalable baseline frameworks that support and accelerate Product Development Lifecycle (PDLC) optimization initiatives across the organization.
The role is responsible for ensuring the use of AI and big data to aggregate observational data (from monitoring systems output, job logs, syslog, etc.) and engagement data (from ticketing, incident, and event recording system data) to produce a virtuous circle of continuous insights yielding continuous improvements and fixes.  
This role will lead the implementation of state-of-the-art AI and Generative AI techniques to unlock value from enterprise data, developing innovative approaches that improve operational efficiency and decision-making. Leveraging a combination of technical expertise, analytical judgment, and experimentation, the role will evaluate, select, and implement the most effective technologies, methodologies, and solutions to support business objectives and accelerate continuous improvement initiatives.

What you’ll be doing

• Explore, understand, and implement the most recent machine learning algorithms and approaches for supervised and unsupervised machine learning and deep learning.
• Develop and implement machine learning algorithms and models. (Regression, classification, clustering, etc).
• Use AI and big data to identify opportunities and work with partner teams to optimise operations.
• Design and optimise scalable AI pipelines for processing and analysing large-scale datasets on the AWS platform.
• Conduct research to stay up to date with the latest advancements in AI.
• Troubleshoot and optimise AI models and pipelines for performance and accuracy.
• Identify and develop trusted adviser relationships with relevant stakeholders, management sponsors and senior executives.  
• Build and maintain trusted advisor relationships with key stakeholders, management sponsors, and senior executives, providing strategic guidance on AI-driven transformation, operational optimization, and technology innovation

Essential Skills / Experience

• Proven hands-on experience in designing, developing, and deploying Machine Learning and Deep Learning solutions within enterprise environments.
• Strong understanding of Generative AI technologies, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI-powered automation frameworks.
• Experience building and managing scalable platforms and data pipelines capable of processing and analyzing large volumes of structured and unstructured data.
• Strong programming expertise in Python and/or Java, with experience developing production-grade AI and data-driven applications.
• Demonstrated experience deploying and managing AI/ML solutions on AWS, including services such as SageMaker, Bedrock, Lambda, S3, EC2, EKS, and ECS.
• Experience developing innovative solutions from concept through implementation, including architecture design, experimentation, deployment, and continuous optimization.
• Strong understanding of DevOps principles, containerization technologies, and cloud-native architectures.
• Ability to collaborate effectively with cross-functional teams and influence stakeholders at all levels while driving AI-led transformation initiatives.

Desirable Skills / Experience

• Experience working with Agentic AI frameworks and multi-agent architectures.
• Knowledge of Amazon SageMaker for machine learning model development, training, and deployment.
• Experience implementing and managing CI/CD pipelines to support automated testing, deployment, and release processes.
• Familiarity with Apache Airflow or similar workflow orchestration tools for managing data and AI pipelines.
• Understanding of Quality Assurance (QA) methodologies, testing frameworks, and best practices for AI/ML solutions.
• Exposure to MLOps practices, including model monitoring, versioning, and lifecycle management.
• Experience with workflow automation, model operationalization, and production-scale AI deployments.
• Knowledge of AI governance, model validation, and responsible AI practices.
• Familiarity with Agile delivery methodologies and modern software engineering best practices.