Data AI Architect

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