Educational Requirements

Bachelor of Engineering

Service Line

Infosys Quality Engineering

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: Partner with product and engineering teams to identify Data for AI opportunities and shape roadmaps

- Support client workshops, RFPs, and solution presentations

- Mentor engineers on AI/ML/Gen AI best practices and emerging technologies

- Translate complex AI concepts into business-friendly narratives

Technical and Professional Requirements

- Must Have Qualifications: 13+ years of experience in software engineering with 3+ years in AI with robust architecture ownership

- Strong expertise in data engineering, data quality, and data governance

- Experience supporting AI use cases such as RAG, feature engineering, and model training

- Proficiency with data platforms, cloud services, and distributed data systems

- Solid understanding of QE practices related to data validation and testing

Good to Have Skills

- Experience with Generative BI or AI assisted analytics

- Knowledge of metadata management, lineage tools, and data observability

- Exposure to AI ethics and bias in data sets

- Cloud data certifications

Preferred Skills

- Technology->Architecture->Architecture - ALL

- Technology->ETL Data Quality->IBM Infosphere Datastage->IBM Infosphere Datastage - Datastage

- Technology->Data Management->Data Architecture->Data Architecture - Metadata Management

- Technology->Enterprise Architecture->Digital Architecture

- Technology->AI-AI Engineering->AI/ML Solution Architecture and Design

- Technology->Data Engineering->Databricks

- Technology->AI-Data science->Databricks Machine Learning

- Technology->AI-AI Engineering->Databricks AI Engineering Services

- Technology->AI-AI Engineering->LLMOps

📌 Data Engineering AI Architect (Bengaluru)
🏢 Infosys
📍 Bengaluru