Cloud, Data Science & AI Architect

Capgemini Engineering; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Capgemini Engineering Bengaluru is seeking a senior architect to own end-to-end cloud and data platform architectures. You will lead scalable cloud-native solutions across AWS, Azure and Google Cloud, spanning data pipelines, data governance and analytics for enterprise AI initiatives.

The role emphasizes design of data-lake/warehouse and lakehouse patterns, MLOps, and Generative AI integration with modern tech such as Databricks, Snowflake and LangChain-inspired frameworks.

Qualifications

• Extensive experience designing scalable, secure and highly available enterprise solutions on cloud platforms.

Responsibilities

• Define and own the end-to-end architecture for enterprise cloud, data, analytics, ML and Generative AI platforms.
• Architect and lead development of scalable cloud and data platforms for digital transformation and AI initiatives.
• Design cloud-native, distributed, microservices-based architectures on AWS/Azure/GCP.
• Define scalable data architectures for batch, streaming, event-driven and real-time processing.
• Design data platforms for ingestion, transformation, storage, governance, analytics and consumption.
• Architect data-lake, data-warehouse and lakehouse solutions using Databricks, Snowflake, BigQuery, Synapse or Fabric.
• Design cloud-native data products, APIs and reusable services for BI, analytics and AI apps.
• Lead architecture and deployment of ML solutions: model development, deployment, monitoring and lifecycle.

Skills

Cloud architecture

AWS

Azure

GCP

Databricks

Snowflake

MLflow

SageMaker

Python

SQL

Tools

Databricks

Snowflake

BigQuery

Synapse

Fabric

Kafka

Kinesis

Job description

• 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.

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.

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.
• 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 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.