Within the Data & AI team, you design and industrialize agent-based and NLP systems capable of reasoning, planning, and acting autonomously.

Your mission is to transform generative AI technologies into robust, scalable products delivering measurable business value.

You drive the agentic vision across the organization and structure governance, performance measurement, and cost control of AI systems.
 

Responsabilities :

1. Scoping & Architecture

• Translate business needs into structured technical specifications
• Design multi-agent architectures and define orchestration mechanisms
• Develop autonomous agents integrated into existing workflows
• Define performance KPIs for agentic systems
• Implement governance frameworks (security, traceability, auditability)

2. Generative AI & NLP
 

• Build chatbots and document-based intelligent agents
• Evaluate and implement LLM strategies adapted to use cases:
  • RAG
  • GraphRAG
  • Lightweight fine-tuning
  • Query rewriting
• Manage embeddings, semantic search, and indexing pipelines
• Process structured and unstructured data (PDFs, standards, emails, multilingual corpora)
• Address bias mitigation and cultural adaptation in language models
   

3. Industrialization & Performance
 

• Deploy and monitor NLP models in production (APIs, microservices, CI/CD)
• Implement advanced observability (logs, traces, metrics)
• Optimize large-scale token consumption and cost management
• Ensure data security, GDPR compliance, and access governance
• Rapidly prototype agent workflows to validate technical feasibility before scaling
   

4. Collaboration & Leadership
 

• Work closely with AI Engineers and Data Scientists on:
  • Model selection
  • Fine-tuning strategies
  • Evaluation frameworks
• Lead structured technology watch and share innovations internally
• Contribute to technical community engagement, mentoring, and conferences

Qualifications:

• Master’s degree (or equivalent) in Computer Science or Artificial Intelligence
• 4–6+ years of experience in AI / NLP (or demonstrable project-based expertise)
• Hands-on experience with hosted or self-managed LLMs
• Practical expertise in embeddings, RAG pipelines, evaluation, and observability
• Proven experience with Azure cloud environments

Skills:

• Strong Python expertise (FastAPI, Flask, data tooling)
• Proficiency in C# or C++ for complex AI applications
• REST API architecture, SDKs, and system integration
• Agent orchestration frameworks (e.g., LangChain or equivalent)
• HuggingFace, MLflow, and open-source AI tooling
• Advanced PostgreSQL (SQL)
• Docker and containerization
• Azure ecosystem:
  • Cognitive Services
  • AI Foundry
  • Azure Search
• Knowledge of GCP or AWS is a plus
• Solid MLOps / LLMOps culture (versioning, reproducibility, environment management)
   

Must Have

• Proficiency in Python for developing computer vision algorithms.
• Knowledge of deep learning frameworks (PyTorch, TensorFlow).
• Ability to read and understand scientific articles in English.

Nice to Have

• Azure IaaS infrastructure expertise
• Team leadership or Tech Lead experience
• End-to-end AI solution delivery (scoping, estimation, development, audit, optimization)
• Voice AI / TTS technologies
• Microsoft AI-102 – Azure AI Engineer Associate certification

Certifications (Nice to Have)

• MS AI-102 – Azure AI Engineer Associate
   

Soft Skills:

• Structured technological curiosity
• Ability to explain complex AI concepts to non-experts
• Autonomy and rigor
• Strong product mindset and business impact orientation
• Collaborative mindset