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Data Engineering AI Architect
Actively Reviewing
Infosys
Job Description
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 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
- 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
Required Skills
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