Cloud, Data Science & AI Architect

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.

Experience designing batch, near-real-time and real-time data-processing solutions.

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 with prompt engineering, model orchestration, grounding, evaluation, guardrails and responsible-AI practices.

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.

Strong understanding of API management, service integration and event-driven integration patterns.

Experience with Kubernetes, Docker, serverless computing and container-based deployment architectures.

Familiarity with modern CI/CD, DataOps, MLOps, Infrastructure as Code and DevSecOps practices.

Hands-on experience with Terraform, CloudFormation, Bicep or equivalent automation technologies.

Strong knowledge of enterprise data governance, metadata management, lineage, data quality, master-data management and access controls.

Experience with cloud and data security, including encryption, identity and access management, key management, network security and secure data sharing.

Understanding of regulatory, privacy and compliance requirements applicable to enterprise data and AI platforms.

Familiarity with business-intelligence and visualisation platforms such as Power BI, Tableau or Looker.

Experience in the energy, utilities, manufacturing, rail, industrial or other asset-intensive industries would be advantageous.

Bangalore, IN