Description

Architect and own the data foundations, quality frameworks, and integration patterns that enable scalable AI and Generative BI workloads.

This role is on-site.

Responsibilities

• Design data architectures for AI use cases, including ingestion, transformation, RAG support, embeddings, and feature stores.
• Define and enforce standards for data quality, lineage, versioning, and governance across AI workloads.
• Architect automated validation and testing frameworks to ensure data correctness, drift detection, and bias monitoring.
• Integrate data platforms with AI services, defining secure access patterns and observability for training and inference.
• Mentor engineers on AI/ML best practices and translate complex technical concepts for business stakeholders.

Required Skills

• 13+ years of software engineering experience, with 3+ years focused on AI.
• Strong expertise in data engineering, data quality, and data governance.
• Proficiency with distributed data systems and cloud services (AWS, Azure).
• Experience supporting AI use cases such as RAG, feature engineering, and model training.
• Knowledge of LLM platforms (AWS Bedrock, Azure OpenAI, Vertex AI) and orchestration frameworks (LangChain, LangGraph).
• Experience with data ingestion tools like Spark and Kafka, and CI/CD tooling (GitHub Actions, Azure DevOps, Jenkins).
• Understanding of data quality frameworks (Great Expectations, Deequ) and vector databases.
• Bachelor's degree in a relevant field.

Preferred Skills

• Experience with Generative BI or AI-assisted analytics (e.g., Power BI with Copilot).
• Knowledge of metadata management, lineage tools, and data observability platforms.

Key Skills

Education

Bachelor's degree