Capgemini
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.