AI Data Architect

As an AI Data Architect, you are the strategic visionary for our account's data ecosystem. With over a decade of experience, you define the high-level blueprints that integrate Generative AI, traditional ML, and massive enterprise data warehouses into a unified, secure platform. You are responsible for the "Buy vs. Build" decisions regarding data tech stacks and ensuring that our data strategy drives clear business ROI while remaining compliant with global regulations.

Key Responsibilities

• Enterprise Architecture: Define the long-term vision for cloud-native AI data platforms (AWS, Azure, GCP).
  

• Responsible AI Frameworks: Architect the enterprise-wide standards for AI data governance, including model explainability and auditability protocols.
  

• Strategic Integration: Design the integration layer between Vector Databases, Graph Databases, and traditional Data Warehouses for GenAI use cases.
  

• Operational Strategy: Define the MLOps and LLMOps roadmap for the account, ensuring seamless deployment and monitoring at scale.
  

• Stakeholder Advisory: Act as a consultant to C-suite and VP-level stakeholders on data roadmap prioritization and cost management.
  

• Innovation: Evaluate emerging technologies in the Spark ecosystem and AI space to maintain Virtusa’s competitive edge.
  

Requirements

• Architectural Tenure: 10+ years in Data/Software Architecture with a focus on large-scale distributed systems.
  

• Multi-Cloud Expertise: Mastery of the data/AI ecosystems of AWS, GCP, and Azure.
  

• Deep Tech Stack: Authority-level knowledge of Apache Spark, Snowflake, BigQuery, and Hadoop ecosystems.
  

• AI/ML Depth: Significant experience supporting LLM/GenAI use cases (Vector embeddings, RAG architectures, evaluation frameworks).
  

• Governance Authority: Expert knowledge of enterprise governance, metadata management, and the EU AI Act/GDPR landscape.
  

• Strategy & ROI: Proven ability to design architectures that balance performance, scalability, and token/infrastructure cost optimization.
  

• Advanced Tooling: Experience with orchestration and evaluation tools like Kubeflow, MLflow, and advanced observability stacks.
  

• Education: Preferably a Master’s or PhD in Computer Science, Data Science, or a related quantitative field.
  

Benefits

• Fully remote work model
  
• Professional training programs – including Udemy and other development plans
  
• Work with a team that’s recognized for its excellence. We’ve been featured in the Deloitte Technology Fast 50 & FT 1000 rankings. We’ve also received the Great Place To Work® certification for five years in a row