Data & AI Architect

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.

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

Solution Architecture and Design

Design end‑to‑end data and AI architecture that encompasses 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.

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.

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.

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.

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 troubleshooting.

Innovation and Continuous Improvement

Stay current with emerging technologies in data engineering, AI, and cloud ecosystems.

Contribute to the development of accelerators, reusable assets, and frameworks for faster solution deployment.

Mentor junior architects and engineers, fostering a culture of technical excellence and innovation.

Participate in industry forums and technology partnerships to enhance the company’s market positioning.

Required Qualifications

Education : Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.

Experience : 10+ years in data and analytics, with at least 5 years in architecture roles across large‑scale enterprise or SI environments.

Technical Skills :

Data Platforms : Snowflake, Databricks, BigQuery, Synapse, Redshift

Programming : Python, SQL, Scala, Java

Soft Skills : Strong communication, stakeholder management, and solution storytelling capabilities.

Benefits

Medical, Dental and Vision Insurance (Subsidized)

Flexible Spending Accounts (Healthcare and Dependent Care)

Short‑Term and Long‑Term Disability

Life and AD&D Insurance

Assistance Program

Matched 401(k) Retirement Savings Plan

Paid Time Off

Xebia is committed to creating an inclusive and diverse environment and is proud to be an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Please feel free to share your preferred pronouns with us during the application process.

All persons hired will be required to verify identity and eligibility to work in North America, and to complete any required employment eligibility forms.

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