• 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.

• As a Principal ML Architect, you will be the senior technical leader for our most ambitious client projects. You will act as a trusted advisor to clients, a mentor to your engineering team, and the chief architect for complex AI systems. This is a hands-on leadership role that combines deep technical expertise with strategic client management. • Lead Technical Strategy & Architecture: Partner with clients to understand their core business challenges and design sophisticated, scalable solutions leveraging machine learning, search, and agentic AI paradigms. • Manage Client Engagements: Serve as the primary technical point of contact for client stakeholders, translating complex concepts, managing expectations, and ensuring project success from a technical and business perspective. • Lead & Mentor Engineering Teams: Guide a team of talented ML engineers, fostering a culture of excellence, providing technical direction, and taking ultimate responsibility for project delivery. • Drive Innovation: Move beyond conventional ML to architect and implement cutting-edge agentic workflows, agent-based modeling, multi-agent systems, and complex RAG pipelines that deliver transformative value. • Oversee the Full Project Lifecycle: Steer projects from initial discovery and architectural design through to deployment, monitoring, and iteration, ensuring solutions are robust, efficient, and production-ready.

Role Description 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. Qualifications - 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. Requirements - 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. 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. Company Description Infomineo is an equal opportunity employer, we prohibit any sort of discrimination (based on color, race, sex, sexual orientation, religion, national origin or any other attributes) in all aspects of employment (recruiting, hiring, wages and salary, promotions, benefits, training and job termination). If you believe you match our requirements and values, we would be happy to hear from you. Visit our website to know more about us, our services and company culture.

Role Description We are looking for a Staff AI Engineer to play a key role in building the core of our AI platform. In this position, you will design and develop production-grade systems that power intelligent automation, agentic workflows, and large-scale retrieval services. This is a highly technical, hands-on role that involves close collaboration with product and platform teams to transform advanced AI concepts into reliable, scalable, and secure solutions used across our enterprise ecosystem. You will also be responsible to: - Design, build, and maintain AI-powered services and APIs, leveraging LLMs (OpenAI, Anthropic, Qwen, OSS models) and custom ML models. - Develop an enterprise-grade agentic framework that enables orchestration, retrieval, and collaboration between multiple AI agents. - Implement and optimize knowledge retrieval systems and agentic search capabilities using vector databases such as Qdrant and ElasticSearch. - Write well-structured, efficient, and testable Python code for production services, experimentation, and internal developer tools. - Build and maintain shared Python libraries and SDKs used across multiple applications and microservices. - Collaborate with cross-functional teams on architecture, internal protocols, and API standards to ensure consistency and reliability across the platform. - Develop and enhance monitoring, validation, and observability for production-grade AI solutions. - Drive the full software development lifecycle - from design and implementation to deployment, monitoring, and continuous improvement. - Identify and resolve performance bottlenecks, reliability issues, and scaling challenges in complex, data-intensive environments. - Participate in code reviews and technical discussions, mentoring other engineers and contributing to a culture of excellence. Qualifications - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - 7+ years of experience as a Software Engineer, with strong proficiency in Python. - Proven track record of building and maintaining production-grade systems using Python. - Strong understanding of distributed systems, API design, and data-driven architectures. - Experience with relational and non-relational databases (PostgreSQL, Elastic, Qdrant, or similar). - Familiarity with AI/ML system design, including LLM integration and evaluation pipelines. - Knowledge of DevOps and observability practices (CI/CD, monitoring, metrics, and model validation). - Python • FastAPI • LLM APIs (OpenAI, Anthropic, Qwen, OSS) • LiteLLM • Qdrant • PostgreSQL • ElasticSearch • Langfuse • Kubernetes • GitHub Actions • ArgoCD Requirements - Experience working with multiple LLM providers (OpenAI, Anthropic, Qwen, open-source models). - Background in developer platforms or AI infrastructure services. - Familiarity with vector databases, semantic retrieval, and knowledge graph architectures. - Exposure to Langfuse, LiteLLM, LangChain, or similar frameworks. - Experience developing enterprise-scale SaaS or distributed backend systems. - Contributions to open-source projects in Python, AI, or infrastructure engineering. Soft Skills / Personal Characteristics - Excellent communication skills, with the ability to convey complex technical ideas clearly to both technical and non-technical audiences. - Collaborative and proactive approach, comfortable working across teams in a dynamic environment. - Strong analytical and problem-solving abilities, with a focus on continuous improvement and innovation. - Curiosity and a genuine interest in emerging AI technologies and modern backend architectures.