via Career pages·Today
Data AI Architect
Infosys
Full-timeOn-site
Location:Bangalore, IndiaType:Full-timePosted:Today
Key Responsibilities
Data Architecture for AI
- Architect AI data foundations including ingestion, transformation, enrichment, and serving layers
- Design data architectures supporting RAG, embeddings, feature stores, and training data pipelines
- Define standards for data quality, lineage, versioning, and governance for AI workloads
- Ensure data platforms support scalability, performance, and low latency AI use cases
Data Quality & Assurance
- Architect data validation and testing frameworks for AI and analytics systems
- Enable automated validation for data correctness, drift, bias, and completeness
- Define test strategies for data migration, data transformation, and AI readiness
- Collaborate with QE teams to embed data assurance into pipelines and platforms
Platform & Integration
- Integrate data platforms with AI services and analytics tools
- Define secure access patterns for data used in training, inference, and evaluation
- Enable observability for data pipelines and AI data consumption
- Guide teams on best practices for AI enabled BI and data driven systems
Core Platforms, Frameworks & Tooling
- LLM and foundation model platforms (e.g., AWS Bedrock, Azure OpenAI, Vertex AI)
- Agentic AI and orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, Google ADK or equivalent)
- CI/CD and MLOps tooling for AI pipelines (GitHub Actions, Azure DevOps, Jenkins)
- Data ingestion and processing platforms (Spark, Kafka, cloud native ETL/ELT frameworks)
- Data quality and validation frameworks (Great Expectations, Amazon Deequ, custom reconciliation frameworks)
- Feature stores and embedding pipelines (Feast, embedding generation pipelines, vector databases)
- Data drift, bias, and consistency monitoring tools (Evidently, statistical data quality monitors)
- Metadata, lineage, and governance platforms (DataHub, Apache Atlas, cloud data catalogs)
- AI enabled analytics and Generative BI platforms (Power BI with Copilot, semantic layers, NLQ enabled BI)
- Cloud native data platforms and storage (object storage, distributed query engines, data lakehouses)
Client Orientation & Leadership
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