Educational Requirements;
Bachelor of Engineering

Service Line;
- Infosys Quality Engineering

- Responsibilities;
- Key ResponsibilitiesData Architecture for AIArchitect AI data foundations including ingestion, transformation, enrichment, and serving layersDesign data architectures supporting RAG, embeddings, feature stores, and training data pipelines Define standards for data quality, lineage, versioning, and governance for AI workloadsEnsure data platforms support scalability, performance, and low latency AI use casesData Quality AssuranceArchitect data validation and testing frameworks for AI and analytics systemsEnable automated validation for data correctness, drift, bias, and completeness Define test strategies for data migration, data transformation, and AI readinessCollaborate with QE teams to embed data assurance into pipelines and platformsPlatform IntegrationIntegrate data platforms with AI services and analytics toolsDefine secure access patterns for data used in training, inference, and evaluationEnable observability for data pipelines and AI data consumptionGuide teams on best practices for AI enabled BI and data driven systemsCore Platforms, Frameworks ToolingLLM 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 LeadershipPartner with product and engineering teams to identify Data for AI opportunities and shape roadmapsSupport client workshops, RFPs, and solution presentationsMentor engineers on AI/ML/Gen AI best practices and emerging technologiesTranslate complex AI concepts into business-friendly narratives
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Technical and Professional Requirements:;
- Must Have Qualifications13+ years of experience in software engineering with 3+ years in AI with solid architecture ownershipStrong expertise in data engineering, data quality, and data governanceExperience supporting AI use cases such as RAG, feature engineering, and model trainingProficiency with data platforms, cloud services, and distributed data systemsSolid understanding of QE practices related to data validation and testingGood to Have SkillsExperience with Generative BI or AI assisted analytics Knowledge of metadata management, lineage tools, and data observabilityExposure to AI ethics and bias in data setsCloud data certifications

Preferred Skills:;

- Technology- Embedded Software- Matlab
- Technology- Data Management- Data Architecture- Data Architecture - Data Modeling
- Technology- Agile Testing- Agile Testing - ALL- CD/CI
- Technology- Big Data - Data Processing- Spark
- Technology- Machine Learning- Generative AI- retrieval augmented generation (rag)
- Technology- Data Engineering- Databricks
- Technology- Data Engineering- Palantir Foundry
- Technology- Integration- Confluent Kafka

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📌 Data AI Architect (Bengaluru)
🏢 Infosys
📍 Bengaluru