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