Time since we first saw it
Day 9
Similar jobs usually come down between day 8 and day 57.
From 9,513 similar postings that came down. Coming down is not always a hire.
Cloud, Data Science & AI Architect
Bangalore, IN
Actively hiring, first seen by us 9 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDefine and own end-to-end architecture for cloud, data, ML and GenAI platforms
Design cloud-native data platforms, lakehouses and streaming pipelines
Lead ML deployment, MLOps and model lifecycle management
Lead design of Generative AI solutions using LLMs, RAG and agents
Architect real-time analytics using Kafka, Kinesis, Flink or Spark Streaming
From the employer’s posting
Define and own the end-to-end architecture for enterprise cloud, data, analytics, machine learning and Generative AI platforms.
Design enterprise data platforms covering data ingestion, transformation, storage, metadata management, governance, analytics and consumption.
Lead the architecture and deployment of machine-learning solutions, including model development, feature engineering, deployment, monitoring, retraining and lifecycle management.
Lead the design of Generative AI solutions using large language models, retrieval-augmented generation, vector databases, prompt engineering and agent-based frameworks.
Architect streaming and real-time analytics solutions using Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink, Spark Streaming or equivalent technologies.
Posting record
Report an errorActively hiringFirst seen by us 9 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 5, 20269 days ago
- Recorded sightings
- 5
- Last seen by us
- Nov 14, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Capgemini Service SAS is hiring
284 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Go
- Java
- Python
- SQL
- AWS
- Azure
- BigQuery
- Databricks
- Kafka
- MLflow
- NoSQL
- SageMaker
- Snowflake
- Spark
- GCP
- Node.js
Where the posting names them
- 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.
- 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.
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.
- 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.
- 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 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Your next step
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About Capgemini Service SAS
- People
- About 11,267 in 2024.Benefit plan filings (Form 5500)
- Company
- Listed as CAP.PA.
- H-1B visas
- 771 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
Demand for Data Leadership roles
New Data Leadership postings at the employers we follow are about the same over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
US government figures for computer and information systems managers
- Median yearly pay, May 2025
- $175,140
- Expected change, 2025 to 2035
- 15.8% more jobsagainst 3.5% for all jobs
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Job description
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bangalore, IN
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.