4 days ago
Bengaluru, IndiaSenior
Responsibilities
- Define and own end-to-end architectures for enterprise cloud, data, analytics, machine learning, and Generative AI platforms.
- Architect scalable cloud-native, distributed, event-driven, and microservices-based solutions on AWS, Microsoft Azure, or Google Cloud Platform.
- Design data lakes, warehouses, lakehouses, ingestion and transformation platforms, governance systems, data products, APIs, and reusable services.
- Lead machine-learning solution architecture, including feature engineering, deployment, monitoring, retraining, and lifecycle management.
- Define and implement MLOps platforms and architectures.
- Design Generative AI, RAG, intelligent assistant, copilot, autonomous-agent, and multi-agent solutions.
- Architect batch, streaming, event-driven, and real-time data processing using enterprise data and messaging ecosystems.
- Establish data modeling, indexing, partitioning, caching, integration, and performance-optimization standards.
Requirements
- 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 enterprise data-platform architecture, including ingestion, processing, storage, governance, analytics, and consumption.
- Strong experience with data-lake, data-warehouse, and lakehouse architecture patterns and platforms such as Apache Spark, Databricks, Snowflake, BigQuery, Azure Synapse Analytics, or Microsoft Fabric.
- Strong experience with relational, NoSQL, and analytical databases, including data modeling, indexing, partitioning, and performance optimization.
- Hands-on experience with Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink, or Spark Streaming.
- Strong understanding of machine-learning and AI lifecycle management and experience implementing enterprise MLOps platforms.
- Hands-on experience with MLflow, Azure Machine Learning, Amazon SageMaker, or equivalent platforms.
- Strong experience building Generative AI applications using large language models and retrieval-augmented generation.
- Experience with LangChain, LangGraph, Semantic Kernel, or equivalent agent-based frameworks.
- Experience with vector databases and semantic-search platforms such as Pinecone, Weaviate, Azure AI Search, OpenSearch, or pgvector.
- Proficiency in Python and SQL, plus working knowledge of Java, Golang, or Node.js.
- Experience developing and deploying cloud-native APIs, microservices, and data services.
Tech Stack
Apache FlinkApache KafkaApache SparkAWSAzureDatabricksGoGoogle BigQueryGoogle Cloud PlatformJavaMLflowNode.jsPythonSnowflakeSQL
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.