Data AI Architect

Key Responsibilities:

• 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

Technical 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->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag),Technology->Data Engineering->Databricks,Technology->Data Engineering->Palantir Foundry,Technology->Data Management->Data Architecture->Data Architecture - Data Modeling,Technology->Embedded Software->Matlab,Technology->Agile Testing->Agile Testing - ALL->CD/CI,Technology->Integration->Confluent Kafka,Technology->Big Data - Data Processing->Spark