Data For AI Architect

Job description

**Role & 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 lowlatency 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 AIenabled BI and datadriven 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, cloudnative 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)* - AIenabled analytics and Generative BI platforms *(Power BI with Copilot, semantic layers, NLQenabled BI)* - Cloudnative 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 **MustHave 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 **GoodtoHave Skills** - Experience with Generative BI or AIassisted analytics - Knowledge of metadata management, lineage tools, and data observability - Exposure to AI ethics and bias in data sets - Cloud data certifications