Analytics & AI Architect

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Infomineo

WebsiteLinkedInAll Job Openings

201 - 500 employees

Infomineo is the global leader in the business of "Brainshoring".

📋 Description

• Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment: • 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. • 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. • 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. • Provide internal training and knowledge-sharing sessions with the team. • Support the Head of Practice on business development and internal capability initiatives.

🎯 Requirements

• 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. • 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). • 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). • 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. • 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. • Strong ability to work and collaborate with a variety of stakeholders across technical and business functions.

🏖️ Benefits

• A competitive salary. • A great working environment. • A steep learning curve with interesting and diverse topics to work on. • A healthy work-life balance. • Health insurance benefits.