Cloud, Data Science & AI Architect
Capgemini
- Location
- Bengaluru, Karnataka, India
- Job type
- Full-time
Required skills
- Tableau
- Looker
- LangChain
- Python
- Power BI
- advanced analytics
- AWS
- Apache
- Apache Flink
- Apache Kafka
- Apache Spark
- Azure
- BigQuery
- Business Intelligence
- caching
- CloudFormation
- compliance
- data ingestion
- data science
- Databricks
- Docker
- end-to-end
- enterprise solutions
- Flink
- Golang
- Google Cloud
- infrastructure-as-code
- Java
- JS
- Kafka
- kernel
- Kubernetes
- machine learning
- metadata management
- network security
- Node
- NoSQL
- Serverless
- Snowflake
- Spark
- Terraform
About the role
Capgemini
Website:
capgemini.com
Company:
https://www.linkedin.com/company/capgemini
Seniority: Executive
Industries: IT Services and IT Consulting
Job details:
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.
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