Data AI Architect

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

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