AI Architect

Employer Overview

The company is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence. They have evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise. They offer 3 services to their global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights, and Resource Scaling.

Job Summary

This role will give you the opportunity to design and shape the technical foundation of our Analytics & AI practice, working on high-impact client engagements across industries. As our Analytics & AI Architect, you will sit at the intersection of data engineering, analytics, data science, and AI, defining the standards, frameworks, and architectures that our teams build upon.

Key Responsibilities:

You will lead the technical architecture of Data, Analytics, and AI solutions for our clients, covering the full lifecycle from design to deployment:

Architecture & Design

• Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines.
• Define standards for data modeling, storage, ingestion, and transformation across client engagements.
• Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring). ● Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling.

Team Enablement

• Define best practices and reusable frameworks for data engineers, analysts, and data scientists.
• Act as a technical mentor and reviewer for cross-functional project teams.
• Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects.
• Contribute to internal knowledge base, toolkits, and delivery accelerators.

Client Engagement

• Lead architecture workshops and discovery sessions with client stakeholders.
• Translate business requirements into scalable, robust technical blueprints.
• Present architecture decisions to both technical teams and executive audiences.
• Support pre-sales and proposal efforts with technical scoping and solution design.

Other

• Provide internal training and knowledge-sharing sessions with the team.
• Support the Head of Practice on business development and internal capability initiatives.

Qualifications

Education & Professional Experience:

• Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related field.
• Full proficiency in English + 1 additional language (French, Arabic, Spanish, German...).
• 6+ years of technical experience in data architecture or a closely related field.
• Proven track record in a consulting or multi-client services environment.

Technical Skills:

Data Architecture & Platforms

• Proven hands-on experience designing large-scale data platforms: data lake, lakehouse, or warehouse architectures (Databricks, Snowflake, BigQuery, Azure Synapse, Redshift).
• Strong command of SQL and at least one of Python, Scala, or Spark for data processing and transformation.
• Experience with Big Data ecosystems: Hadoop, Spark, PySpark, Hive, or equivalent.
• Familiarity with streaming and real-time architectures (Kafka, Flink, Spark Streaming).

AI & ML Infrastructure

• Proven hands-on experience with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Azure ML, or equivalent.
• Experience architecting MLOps pipelines: model versioning, CI/CD for ML, monitoring and drift detection.
• Exposure to GenAI and LLM integration patterns (RAG architectures, vector databases, prompt pipelines).

Data Engineering & Deployment

• Proven hands-on experience with orchestration and transformation tools: Airflow, dbt, or equivalent.
• Proven hands-on experience with container technologies: Docker, Kubernetes.
• Proven hands-on experience with versioning software: Git, GitHub, GitLab.
• Proven hands-on experience deploying solutions in cloud ecosystems: AWS, Azure, or Google Cloud.

Governance & Standards

• Knowledge of data governance frameworks: data catalogs, lineage tracking, access control, and data quality management.
• Exposure to BI and data visualization platforms (Power BI, Tableau, Looker) and semantic layer design.

Interpersonal Skills

• Ability to step back, analyze complex problems, define architectural options, and drive decisions.
• Strong ability to work and collaborate with a variety of stakeholders across technical and business functions.
• Excellent communication skills with the ability to translate complex technical architectures into clear business implications.
• High autonomy, attention to detail, and ability to manage multiple client engagements simultaneously.