AI & Analytics - AI Architect - Freelance - Casablanca
Infomineo
| Company | Infomineo |
| Category | Data & Analytics |
| Location | Casablanca |
| Remote | Remote |
| Employment | Not stated |
| Level | Lead |
| Salary | Not stated by the employer |
| Posted | 13 Jul 2026 |
| Last verified | 10 Aug 2026 |
| Source | Employer ATS (workable) |
Description
About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence. We’ve evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise. We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling. This is made possible by relying on 3 pillars of excellence: 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona). Our proprietary AI orchestrator. Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions. Ready to kick start your career with us? About this role 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. What will you do? 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. Who are you? EDUCATION & PROFESSIONAL EXPERIENCE Master’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,