Infomineo

AI & Analytics Architect (Freelance)

Casablanca

Freelance / IndépendantTélétravailMaster / Bac+5Anglais, Français, Arabe, Espagnol, AllemandData / BIInformatique / IT

L’essentiel du poste

Résumé par Postule AI

Infomineo seeks an experienced AI & Analytics Architect to design and lead the technical foundation of data, analytics, and AI solutions for global clients. This freelance remote role requires 6+ years in data architecture, expertise in cloud platforms (Azure, AWS, GCP), MLOps infrastructure, and proven consulting experience. You'll architect end-to-end data solutions, mentor cross-functional teams, and engage directly with clients on high-impact engagements across industries.

Vos missions

  • Design end-to-end data architectures including data lakes, lakehouses, warehouses, and streaming pipelines for client engagements
  • Define technical standards and frameworks for data modeling, storage, ingestion, transformation, and MLOps across projects
  • Lead architecture workshops with client stakeholders and translate business requirements into scalable technical blueprints
  • Mentor cross-functional teams, act as technical reviewer, and bridge gaps between data engineers, analysts, and AI/ML engineers
  • Support pre-sales efforts with technical scoping, solution design, and proposal development

Profil recherché

  • You have 6+ years architecting large-scale data platforms in consulting or multi-client services
  • You excel at translating complex technical architectures into clear business implications for stakeholders
  • You are autonomous, detail-oriented, and can manage multiple client engagements simultaneously
  • You have hands-on expertise with modern data stack tools and cloud ecosystems
  • You are passionate about mentoring teams and establishing best practices and reusable frameworks

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About the Role

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

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 including model registries, CI/CD for ML, and 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 technical mentor and reviewer for cross-functional project teams
  • Bridge gaps 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 technical teams and executive audiences
  • Support pre-sales and proposal efforts with technical scoping and solution design

Required Qualifications

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

Technical Skills Required

  • Hands-on experience designing large-scale data platforms: data lakes, lakehouses, warehouses (Databricks, Snowflake, BigQuery, Azure Synapse, Redshift)
  • Strong SQL and proficiency in Python, Scala, or Spark for data processing
  • Experience with Big Data ecosystems: Hadoop, Spark, PySpark, Hive
  • Hands-on experience with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Azure ML
  • Experience architecting MLOps pipelines with model versioning, CI/CD for ML, monitoring, and drift detection
  • Hands-on experience with orchestration tools: Airflow, dbt
  • Proficiency with Docker, Kubernetes, Git, and cloud deployment (AWS, Azure, GCP)
  • Knowledge of data governance frameworks and BI platforms (Power BI, Tableau, Looker)

What We Offer

  • Competitive salary
  • Great working environment with steep learning curve
  • Diverse and interesting projects across industries
  • Healthy work-life balance
  • Health insurance benefits

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