AI Engineer / Agentic AI Engineer
Paris
Posted 1d ago
Job Location
Paris
Tech Stack
Remote Work Policy
On-site
Categories
AI Agent Engineer
About the job
We are seeking an AI Engineer / Agentic AI Engineer to lead the design and implementation of advanced AI, Machine Learning, Generative AI, and Agentic AI solutions for major clients. In this role, you will be responsible for key client project streams, from problem framing and architecture definition to deployment and adoption. You will act as a bridge between business needs and technology, translating client challenges into scalable AI solutions and guiding architectural choices between ML, GenAI, and agentic approaches. Your contributions will extend beyond solution building to include defining, positioning, and driving adoption, involving technical discussions, commercial proposals, and advising stakeholders on the most relevant approaches, from LLM-based applications like RAG and copilots to agentic systems involving multi-agent architectures and autonomous processes. You will work closely with senior stakeholders, architects, and cross-functional teams in a client-facing capacity with growing autonomy, while also contributing to reusable assets and best practices in GenAI and Agentic AI.
Responsibilities
- Lead the design, development, and validation of machine learning models aligned with business objectives, ensuring scalability, robustness, and measurable impact.
- Design and deliver end-to-end Generative AI solutions, including RAG architectures, prompt engineering strategies, and enterprise-level LLM applications.
- Design and implement agentic AI systems, encompassing multi-agent architectures, tool usage, planning and reasoning workflows, and knowledge graph integration.
- Make architectural decisions, selecting appropriate approaches among ML, GenAI pipelines, and agentic systems based on business context, constraints, and risks.
- Evaluate and select foundational models (e.g., OpenAI, Anthropic, Google Gemini, Mistral) and define architectures combining GenAI and agentic systems.
- Design and implement vector databases and graph-based search systems, embedding pipelines, and retrieval mechanisms; design knowledge graphs, memory layers, and state management for advanced workflows.
- Define and implement LLMOps and AgentOps frameworks, including deployment pipelines, orchestration, monitoring, evaluation, and lifecycle management.
- Design and implement guardrails, security mechanisms, and human-in-the-loop processes for generative and agentic systems.
- Lead the development of Proofs of Concept (PoCs) and Minimum Viable Products (MVPs) and their industrialization into reliable, governed production systems.
- Facilitate client workshops, technical discussions, and design sessions; advise on trade-offs between ML, GenAI, and Agentic AI.
- Collaborate with multidisciplinary teams (engineering, cloud, data, security) to deliver integrated solutions.
- Contribute to commercial proposals, client deliverables, and AI transformation roadmaps.
- Develop reusable assets, accelerators, and best practices.
Requirements
- Degree (Bachelor's or Master's) in computer science, data science, machine learning, mathematics, physics, or a related field.
- 2 to 4 years of experience in a consulting firm, with end-to-end delivery of AI/ML and GenAI projects.
- Experience with client-facing AI or GenAI initiatives.
- Excellent command of Python and data science/ML ecosystems.
- Experience in developing, evaluating, and optimizing ML/Deep Learning models (TensorFlow, PyTorch, JAX, ONNX).
- Practical experience in prompt engineering, RAG systems, vector databases, and advanced retrieval (hybrid, Graph RAG).
- Experience with agentic AI systems, including multi-agent architectures, tool usage, planning/reasoning workflows, and frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, PydanticAI).
- Proficiency in data pipelines, feature engineering, data quality, SQL, and modern platforms (Databricks, Snowflake).
- Experience with vector databases and graph databases (Neo4j, Amazon Neptune) for advanced use cases.
- Experience in LLMOps/AgentOps, including deployment, monitoring, evaluation, lifecycle management, CI/CD, and containerization.
- Hands-on experience with Azure, AWS, or GCP, including their AI/ML services.
- Ability to structure problems and translate business needs into AI solutions.
- Ability to explain complex architectures to diverse audiences.
- Analytical and structured problem-solving skills.
- Ability to lead streams, take responsibility, and mentor junior profiles.
- Adaptability in fast-paced and evolving environments.
- Fluent written and oral French and English.