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 and Professional 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
