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.

- 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.

- Solid 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.

📌 Cloud, Data Science & AI Architect (Bengaluru)
🏢 Capgemini
📍 Bengaluru