Data Engineering AI Architect

Infosys; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Infosys is seeking an experienced data architect to lead AI data foundations and end-to-end data pipelines in Bengaluru. You will define data quality, governance, and observability standards while partnering with QE teams to ensure robust AI workloads.

This role emphasizes scalable, low-latency data platforms and collaboration across product and engineering teams. The ideal candidate brings 13+ years in software engineering with AI experience and a deep background in data platforms, cloud

Qualifications

• 13+ years of software engineering with 3+ years in AI and 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.

Responsibilities

• Architect data foundations for AI, including ingestion, transformation, enrichment, and serving layers.
• Design data architectures supporting RAG, embeddings, feature stores, and training 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.
• Collaborate with QE teams to embed data assurance into pipelines and platforms.
• CI/CD and MLOps tooling for AI pipelines (GitHub Actions, Azure DevOps, Jenkins).
• Data ingestion and processing platforms (Spark, Kafka, cloud native ETL/ELT).

Skills

Data architecture

Data governance

AI/ML engineering

RAG & embeddings

Data quality

Observability

MLOps

Communication

Education

Bachelor of Engineering

Tools

Spark

Kafka

Databricks

Great Expectations

Feast

Power BI

GitHub Actions

Azure DevOps

Jenkins

Job description

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 strong 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