Data AI Architect

Data AI Architect

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