{"id":3456979,"url":"https://alion.io/job/capgemini-cloud-data-science-ai-architect","title":"Cloud, Data Science & AI Architect","company":{"id":145,"name":"Capgemini","domain":"capgemini.com","url":"https://alion.io/company/capgemini","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":80,"open_postings":1815,"ghost_share":0.002,"stale_share":0.807,"repost_share":0.013,"time_to_fill_p50_days":29,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, 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platforms.\nArchitect and lead the development of scalable cloud and data platforms supporting digital transformation, business intelligence, advanced analytics and AI initiatives.\nDesign cloud-native, distributed and microservices-based solution architectures on AWS, Microsoft Azure or Google Cloud Platform.\nDefine scalable data architectures for batch, streaming, event-driven and real-time processing workloads.\nDesign enterprise data platforms covering data ingestion, transformation, storage, metadata management, governance, analytics and consumption.\nArchitect data-lake, data-warehouse and lakehouse solutions using platforms such as Databricks, Snowflake, Microsoft Fabric, BigQuery, Synapse or equivalent technologies.\nDesign cloud-native data products, APIs and reusable services that enable business intelligence, advanced analytics and AI applications.\nLead the architecture and deployment of machine-learning solutions, including model development, feature engineering, deployment, monitoring, retraining and lifecycle management.\nDefine and implement MLOps architectures using platforms and tools such as MLflow, Azure Machine Learning, Amazon SageMaker or equivalent technologies.\nLead the design of Generative AI solutions using large language models, retrieval-augmented generation, vector databases, prompt engineering and agent-based frameworks.\nDefine AI orchestration patterns for intelligent assistants, copilots, autonomous agents and domain-specific AI applications.\nDesign and optimise enterprise-grade data pipelines to ensure reliable, scalable and high-quality data processing.\nArchitect streaming and real-time analytics solutions using Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink, Spark Streaming or equivalent technologies.\nEstablish data-modelling, indexing, partitioning, caching and performance-optimisation standards for relational, NoSQL and analytical data stores.\nDesign integration frameworks using APIs, event-driven architectures, messaging platforms and enterprise data ecosystems.\nJob Description - Grade Specific\nExtensive experience designing scalable, secure and highly available enterprise solutions on AWS, Microsoft Azure or Google Cloud Platform.\nStrong understanding of cloud-native, distributed, event-driven and microservices-based architectures.\nDeep expertise in designing and implementing enterprise data platforms covering ingestion, processing, storage, governance, analytics and data consumption.\nStrong experience with data-lake, data-warehouse and lakehouse architecture patterns.\nHands-on experience with data-engineering platforms such as Apache Spark, Databricks, Snowflake, Google BigQuery, Azure Synapse Analytics, Microsoft Fabric or equivalent technologies.\nStrong experience with relational, NoSQL and analytical databases, including data modelling, indexing, partitioning and performance optimisation.\nHands-on experience with streaming platforms such as Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink or Spark Streaming.\nStrong understanding of machine-learning and AI lifecycle management, including data preparation, model development, validation, deployment, monitoring, retraining and governance.\nExperience designing and implementing enterprise MLOps platforms and practices.\nHands-on experience with tools such as MLflow, Azure Machine Learning, Amazon SageMaker or equivalent platforms.\nStrong experience building Generative AI applications using large language models and retrieval-augmented generation architectures.\nExperience designing agent-based and multi-agent AI solutions using frameworks such as LangChain, LangGraph, Semantic Kernel or equivalent technologies.\nExperience with vector databases and semantic-search platforms such as Pinecone, Weaviate, Azure AI Search, OpenSearch, pgvector or equivalent technologies.\nProficiency in Python and SQL, together with working knowledge of at least one additional language such as Java, Golang 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