About the role

Within the Strategy & Consulting – AI & Data practice, we are seeking a Data Science Manager to lead the design and delivery of advanced AI, Machine Learning, Generative AI, and Agentic AI solutions for enterprise clients.

In this role, you will lead delivery streams and multidisciplinary teams, translating business challenges into scalable AI solutions and ensuring high-quality execution across the full lifecycle — from problem framing and architecture design to deployment and adoption.

You will contribute to shaping solution architectures and implementation strategies, spanning ML systems, LLM-based applications (RAG, copilots, automation), and emerging agentic systems (multi-agent workflows, tool-augmented reasoning).

Working closely with senior stakeholders, you will play a client-facing role, ensuring alignment between business objectives and technical delivery, while contributing to reusable assets, best practices, and business development initiatives.

Key responsibilities

• Lead the delivery of AI, GenAI, and Agentic AI projects, ensuring successful execution from design to production deployment.
• Translate business requirements into scalable architectures, including ML pipelines, GenAI applications, and agentic workflows.
• Design and implement Generative AI solutions, including RAG architectures, prompt engineering strategies, and enterprise-grade LLM applications.
• Contribute to the design and implementation of Agentic AI systems, including multi-agent architectures, orchestration workflows, and tool-based reasoning.
• Architect and implement hybrid knowledge systems, including vector databases, embeddings pipelines, and knowledge graphs to support retrieval, reasoning, and memory.
• Define and implement LLMOps practices, including evaluation, monitoring, and lifecycle management of GenAI and agentic systems.
• Ensure responsible AI, governance, and safety mechanisms are integrated into solution delivery.
• Lead the development of PoCs, MVPs, and their industrialization into production systems.
• Lead client workshops, technical discussions, and solution design sessions; present architectures and trade-offs.
• Collaborate with cross-functional teams (engineering, cloud, data, security) to deliver integrated solutions.
• Manage and mentor team members (analysts, consultants), ensuring capability development and delivery excellence.
• Support business development activities, including proposal development and client presentations.
• Contribute to internal assets, accelerators, and best practices across GenAI and Agentic AI.

About you

Education & Background:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Mathematics, Physics, or related field.
• 6–9 years of experience in AI, ML, or advanced analytics, including experience in consulting or client-facing environments.
• Proven experience delivering end-to-end AI or GenAI solutions in production environments.
• Experience leading teams and managing project delivery.
• Relevant certifications (Azure, AWS, GCP, ML/AI) or advanced AI/GenAI programs are a plus.

Technical Skills:

• Programming & ML: Strong experience in Python and ML frameworks (e.g.TensorFlow, PyTorch), with solid understanding of production ML systems.
• Generative AI & LLMs: Experience with RAG architectures, prompt engineering, LLM integration, and frameworks (LangChain, LlamaIndex, Semantic Kernel), including hybrid retrieval approaches (vector + graph, Graph RAG).
• Agentic AI: Familiarity with agent-based systems, including multi-agent workflows, orchestration frameworks (LangGraph, AutoGen, CrewAI), and tool-augmented reasoning.
• Knowledge Systems: Experience designing hybrid knowledge architectures combining vector databases and knowledge graphs (e.g., Neo4j, Amazon Neptune) for advanced reasoning and retrieval.
• Data & Engineering: Strong understanding of data pipelines, distributed processing (Spark), and modern data platforms (Databricks, Snowflake).
• LLMOps / AgentOps: Experience with deployment, monitoring, evaluation, and lifecycle management; familiarity with tools such as MLflow, LangSmith, or Weights & Biases.
• Cloud Platforms: Hands-on experience with Azure, AWS, or GCP, including enterprise AI services (Azure OpenAI, AWS Bedrock, Vertex AI).

Soft Skills:

• Client Engagement: Ability to interact with stakeholders and translate business needs into AI solutions.
• Communication: Strong ability to explain complex architectures to technical and non-technical audiences.
• Problem-Solving: Structured thinking and ability to navigate complex challenges.
• Leadership: Ability to lead teams, mentor junior profiles, and ensure delivery excellence.
• Adaptability: Comfortable in fast-paced environments.

Languages:

• French & English required (fluent, written and spoken)

Preferred qualifications:

• Experience delivering production-grade GenAI or agentic systems.
• Experience with agent orchestration frameworks (LangGraph, AutoGen, Semantic Kernel).
• Experience in AI governance, responsible AI, or regulatory frameworks (e.g., AI Act).
• Experience in consulting, digital transformation, or enterprise environments.
• Open-source contributions or publications in AI/GenAI.
• Advanced certifications or specialized training.

Core skills in a nutshell:

AI Strategy • Machine Learning • Generative AI & LLMs • RAG Architectures • Agentic AI • Multi-Agent Systems • Knowledge Graphs • Vector Databases • Hybrid Retrieval • LLMOps • Data Engineering • Cloud Platforms • Solution Architecture • Client Engagement • Team Leadership

What success looks like

• You deliver scalable AI and GenAI solutions that create measurable business impact.
• You successfully lead project teams and ensure high-quality delivery.
• You design robust architectures combining ML, GenAI, and agentic approaches.
• You build strong relationships with client stakeholders.
• You contribute to business development and practice growth.
• You mentor team members and strengthen team capabilities.