Data & AI Architect

About Xebia

With over 20 years of experience, our global network of passionate technologists and pioneering craftsmen deliver cutting-edge technology and game-changing consulting to companies on the brink of transformation. Since 2001, we have grown from a Java company into a full-service digital consulting company with 4500+ professionals working on a worldwide ambition.

We are organized in complementary chapters – teams with a tremendous amount of knowledge and experience within a particular field, such as Agile, DevOps, Data and AI, Cloud, Software Technology, Functional Programming, Low Code, and Microsoft.

We help the world’s top 250 companies and category leaders overcome digital challenges, embrace innovation, adopt new technology, and implement new business models. In addition to high-quality consulting, we also provide offshoring and nearshoring services.

For more details please visit www.xebia.com

 

Role: Data & AI Architect

Location: Washington, DC, USA

Reports to: Global Data & AI Service Line Leader

Collaborates with: Cloud Architects, Business Analysts, Delivery Managers, and Client Technical Teams

 

Overview 

The Data & AI Architect is responsible for defining the end-to-end architecture for data and AI solutions across multiple industries and geographies. This includes designing data platforms, integrating AI capabilities, and ensuring alignment with business objectives, technical standards, and governance frameworks. The role bridges strategy, engineering, and delivery—translating business requirements into robust, production-ready architectures.

Key Responsibilities

1. Solution Architecture and Design

• Design end-to-end data and AI architectures that encompass data ingestion, storage, processing, analytics, and machine learning.
• Define data pipelines, ETL/ELT frameworks, and model deployment architectures on cloud and hybrid environments.
• Architect solutions leveraging cloud-native AI services (AWS SageMaker, Azure AI, Google Vertex AI) and open-source frameworks (TensorFlow, PyTorch, MLflow). 
• Ensure scalability, performance, and cost optimization across all layers of the data and AI stack.
• Create reference architectures and blueprints to standardize solution delivery across global projects.

2. Data Platform Engineering

• Design and oversee implementation of data lakes, data warehouses, and lakehouse architectures using modern data platforms (Databricks, Snowflake, BigQuery, Synapse).
• Define data modeling standards, metadata management, and master data management (MDM) frameworks.
• Collaborate with data engineers to ensure data pipelines are optimized for AI workloads and real-time analytics.
• Implement data quality, lineage, and observability frameworks to ensure trust and reliability in data assets.

3. AI and Machine Learning Integration

• Architect AI/ML pipelines from data preparation to model training, validation, and deployment (MLOps).
• Integrate AI models into enterprise applications using APIs, microservices, and event-driven architectures.
• Evaluate and recommend AI frameworks and tools for specific business use cases, including NLP, computer vision, and generative AI.
• Collaborate with data scientists and ML engineers to ensure models are production-ready, explainable, and compliant.

4. Governance, Security, and Compliance

• Implement data governance frameworks aligned with corporate and regulatory standards (GDPR, CCPA, HIPAA).
• Define AI governance policies for model lifecycle management, fairness, and accountability.
• Ensure data security and privacy through encryption, access controls, and secure data sharing mechanisms.
• Participate in architecture review boards to validate compliance and best practices across projects.

5. Client Engagement and Delivery Support

• Work closely with client stakeholders to understand business challenges and translate them into data-driven AI solutions.
• Lead technical discovery sessions, solution workshops, and proof-of-concepts (POCs).
• Support pre-sales and proposal development, providing architecture inputs, effort estimation, and technical documentation.
• Guide global delivery teams in solution implementation, performance tuning, and