Job Overview

Capgemini Engineering is hiring a Cloud, Data Science & AI Architect for a permanent Software Engineering role in Bangalore. The position owns end-to-end architecture across enterprise cloud, data, analytics, machine learning and Generative AI platforms. The architect will shape scalable solutions and guide their development and deployment to support business intelligence, advanced analytics and digital transformation.

Key Responsibilities

• Define architecture for enterprise cloud, data, analytics, machine learning and Generative AI platforms.
• Design secure, scalable, highly available cloud-native, distributed, event-driven and microservices-based solutions using AWS, Microsoft Azure or Google Cloud Platform.
• Design data architectures for batch, streaming, event-driven and real-time processing, covering ingestion, transformation, storage, metadata, governance, analytics and consumption.
• Architect data-lake, data-warehouse and lakehouse solutions, plus reusable data products, APIs and services for analytics and AI applications.
• Lead machine-learning architecture across model development, feature engineering, deployment, monitoring, retraining and lifecycle management.
• Define MLOps architectures and lead Generative AI designs involving LLMs, RAG, vector databases, prompt engineering and agent-based frameworks.
• Establish standards for data pipelines, modelling, indexing, partitioning, caching and performance optimisation across relational, NoSQL and analytical stores.
• Design API, messaging and event-driven integration patterns for enterprise ecosystems.
• Set best practices for cloud engineering, data engineering, DataOps, MLOps, DevSecOps, security, governance and operational excellence.
• Define CI/CD, automated testing and Infrastructure-as-Code practices, along with observability, reliability, disaster recovery and cost optimisation.
• Collaborate with data scientists, data engineers, cloud engineers, software architects, product teams and business stakeholders.
• Conduct architecture assessments, technology evaluations, proofs of concept and trade-off analyses; develop technical roadmaps and phased implementation strategies.

Required Technical Expertise

• Extensive enterprise solution architecture experience on AWS, Microsoft Azure or Google Cloud Platform.
• Deep expertise in enterprise data platforms and data-lake, warehouse and lakehouse architecture patterns.
• Hands-on experience with Apache Spark, Databricks, Snowflake, Google BigQuery, Azure Synapse Analytics or equivalent data-engineering platforms.
• Strong knowledge of relational, NoSQL and analytical databases, data modelling and performance optimisation.
• Experience designing batch, near-real-time and real-time data processing, including streaming platforms such as Apache Kafka, Amazon Kinesis, Apache Pulsar, Apache Flink or Spark Streaming.
• Understanding of machine-learning lifecycle management and experience designing enterprise MLOps platforms; tools may include MLflow, Azure Machine Learning or Amazon SageMaker.
• Experience building Generative AI applications using LLMs and RAG, including orchestration, grounding, evaluation, guardrails and responsible-AI practices.
• Experience with agent-based and multi-agent frameworks such as LangChain, LangGraph, Semantic Kernel or equivalents.
• 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 cloud-native APIs, microservices and data services; understanding of API management and service integration.
• Experience with Kubernetes, Docker, serverless computing, CI/CD, DataOps, MLOps, Infrastructure as Code and DevSecOps.
• Hands-on experience with Terraform, CloudFormation, Bicep or equivalent automation technologies.

Governance, Security and Operations

The architect will establish approaches for data governance, metadata management, lineage, data quality, master-data management and access controls. The role also requires attention to encryption, identity and access management, key management, network security and secure data sharing.

Cloud, data and AI solutions must address enterprise privacy, regulatory compliance, data sovereignty and responsible-AI requirements. The position also covers monitoring, observability, reliability, high availability, disaster recovery and cost optimisation.

Analytics and Industry Context

Familiarity with business-intelligence and visualisation platforms such as Power BI, Tableau or Looker is relevant. Experience in energy, utilities, manufacturing, rail, industrial or other asset-intensive industries is advantageous.

Architecture Leadership and Collaboration

The role combines technical design with cross-functional leadership. The architect will work with business and technology stakeholders to define target-state architectures, evaluate emerging technologies and recommend adoption strategies. Solution trade-offs and phased delivery plans should align technical decisions with enterprise needs and operational requirements.

Education and Experience

The supplied description does not specify a required degree, academic branch, or numeric minimum or maximum years of experience. It does require extensive experience designing enterprise cloud solutions and strong practical expertise in data platforms, machine learning, MLOps and Generative AI architecture.

Work Location and Employment Type

The role is based in Bangalore and is listed as permanent within Software Engineering. The description does not explicitly state a remote or hybrid arrangement.

How to Apply

Review the requirements and submit your application using the application link for this opening. Apply now through SoftoJobs to explore this opportunity.