AI Engineering Lead

il y a 4 jours

Tunis, Tunis, Tunisie Market-Securities Temps plein
About the Role

We are seeking a hands-on AI Engineering Lead to drive the strategic integration and practical application of AI/ML technologies across our ecosystem. This role involves building, fine-tuning, and deploying AI Agentic Systems while also shaping our long-term AI strategy, engineering standards, and team capabilities.

As an AI Engineering Lead, you will report directly to the Head of Development and participate in:
  • Architect & Build AI Systems : Design and implement end-to-end AI applications using LLMs, multi-agent workflows, and automation frameworks (e.g., LangChain, LangGraph). 
  • Integrate AI into Core Products: Collaborate with product and engineering teams to embed AI capabilities into new and existing products, ensuring scalability, maintainability, and measurable business impact.
  • Lead & Mentor Junior AI Engineers : Technically lead a team of junior AI engineers through hands-on mentorship, Define best practices for model development, experimentation, documentation, and deployment.
  • Support skill development through guidance on ML fundamentals, LLM systems, and production AI engineering. Contribute to the hiring, onboarding, and technical evaluation of new AI team members.
  • Shape AI Processes & Governance: Establish standards for data quality, model validation, responsible AI usage, and deployment workflows.
Requirements 
  • Experience : 5+ years of experience in technical or product leadership, delivering complex, data-intensive systems.
  • Programming & Development: Strong proficiency in Python, JavaScript, C#, and databases (SQL & NoSQL).
  • AI/ML Frameworks: Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-learn, and Transformer-based models.
  • Core AI/ML Competencies
    • Supervised Learning and Unsupervised Learning 
    • NLP & LLMs: Embeddings, RAG, prompt engineering, text classification, sentiment analysis.
    • Model Optimization: Fine-tuning, transfer learning, hyperparameter tuning, evaluation, and validation.
  • Automation & Orchestration Experience with workflow engines and agent-based systems such as N8n, LangChain, LangGraph, MCP, or similar orchestration frameworks.
Technical Fluency
Strong understanding of software engineering best practices, APIs, and cloud platforms (AWS, Azure). Experience with data architectures and building or maintaining data pipelines is highly valued.

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