Cloud, Data Science & AI Architect

Roles & Responsibilities

• Define and own the end-to-end architecture for enterprise cloud, data, analytics, machine learning and Generative AI platforms.
• Architect and lead the development of scalable cloud and data platforms supporting digital transformation, business intelligence, advanced analytics and AI initiatives.
• Design cloud-native, distributed and microservices-based solution architectures on AWS, Microsoft Azure or Google Cloud Platform.
• Define scalable data architectures for batch, streaming, event-driven and real-time processing workloads.
• Design enterprise data platforms covering data ingestion, transformation, storage, metadata management, governance, analytics and consumption.
• Architect data-lake, data-warehouse and lakehouse solutions using platforms such as Databricks, Snowflake, Microsoft Fabric, BigQuery, Synapse or equivalent technologies.
• Design cloud-native data products, APIs and reusable services that enable business intelligence, advanced analytics and AI applications.
• Lead the architecture and deployment of machine-learning solutions, including model development, feature engineering, deployment, monitoring, retraining and lifecycle management.
• Define and implement MLOps architectures using platforms and tools such as MLflow, Azure Machine Learning, Amazon SageMaker or equivalent technologies.
• Lead the design of Generative AI solutions using large language models, retrieval-augmented generation, vector databases, prompt engineering and agent-based frameworks.
• Define AI orchestration patterns for intelligent assistants, copilots, autonomous agents and domain-specific AI applications.
• Design and optimise enterprise-grade data pipelines to ensure reliable, scalable and high-quality data processing.
• Architect streaming and real-time analytics solutions using Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink, Spark Streaming or equivalent technologies.
• Establish data-modelling, indexing, partitioning, caching and performance-optimisation standards for relational, NoSQL and analytical data stores.
• Design integration frameworks using APIs, event-driven architectures, messaging platforms and enterprise data ecosystems.
• Work closely with data scientists, data engineers, cloud engineers, software architects, product teams and business stakeholders to deliver end-to-end solutions.
• Establish architecture standards and best practices for cloud engineering, data engineering, DataOps, MLOps, DevSecOps, security, governance and operational excellence.
• Define modern CI/CD, automated-testing and Infrastructure-as-Code practices using Terraform, CloudFormation, Bicep or equivalent technologies.
• Ensure that cloud, data and AI solutions comply with enterprise requirements for security, privacy, regulatory compliance, data sovereignty and responsible AI.
• Define observability, monitoring, reliability, high-availability, disaster-recovery and cost-optimisation strategies.
• Evaluate emerging cloud, analytics, data science and AI technologies and recommend appropriate enterprise adoption strategies.
• Conduct architecture assessments, technology evaluations, proofs of concept and solution trade-off analyses.
• Collaborate with business and technology stakeholders to define technical roadmaps, target-state architectures and phased implementation strategies.

Job Description - Grade Specific

• Extensive experience designing scalable, secure and highly available enterprise solutions on AWS, Microsoft Azure or Google Cloud Platform.
• Strong understanding of cloud-native, distributed, event-driven and microservices-based architectures.
• Deep expertise in designing and implementing enterprise data platforms covering ingestion, processing, storage, governance, analytics and data consumption.
• Strong experience with data-lake, data-warehouse and lakehouse architecture patterns.
• Hands-on experience with data-engineering platforms such as Apache Spark, Databricks, Snowflake, Google BigQuery, Azure Synapse Analytics, Microsoft Fabric or equivalent technologies.
• Strong experience with relational, NoSQL and analytical databases, including data modelling, indexing, partitioning and performance optimisation.
• Experience designing batch, near-real-time and real-time data-processing solutions.
• Hands-on experience with streaming platforms such as Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink or Spark Streaming.
• Strong understanding of machine-learning and AI lifecycle management, including data preparation, model development, validation, deployment, monitoring, retraining and governance.
• Experience designing and implementing enterprise MLOps platforms and practices.
• Hands-on experience with tools such as MLflow, Azure Machine Learning, Amazon SageMaker or equivalent platforms.
• Strong experience building Generative AI applications using large language models and retrieval-augmented generation architectures.
• Experience with prompt engineering, model orchestration, grounding, evaluation, guardrails and responsible-AI practices.
• Experience designing agent-based and multi-agent AI solutions using frameworks such as LangChain, LangGraph, Semantic Kernel or equivalent technologies.
• Experience with vector databases and semantic-search platforms such as Pinecone, Weaviate, Azure AI Search, OpenSearch, pgvector or equivalent technologies.
• Proficiency in Python and SQL, together with working knowledge of at least one additional language such as Java, Golang or Node.js.
• Experience developing and deploying cloud-native APIs, microservices and data services.
• Strong understanding of API management, service integration and event-driven integration patterns.
• Experience with Kubernetes, Docker, serverless computing and container-based deployment architectures.
• Familiarity with modern CI/CD, DataOps, MLOps, Infrastructure as Code and DevSecOps practices.
• Hands-on experience with Terraform, CloudFormation, Bicep or equivalent automation technologies.
• Strong knowledge of enterprise data governance, metadata management, lineage, data quality, master-data management and access controls.
• Experience with cloud and data security, including encryption, identity and access management, key management, network security and secure data sharing.
• Understanding of regulatory, privacy and compliance requirements applicable to enterprise data and AI platforms.
• Familiarity with business-intelligence and visualisation platforms such as Power BI, Tableau or Looker.
• Experience in the energy, utilities, manufacturing, rail, industrial or other asset-intensive industries would be advantageous.