Tech Lead AI

Overview

Define and evolve AI architecture choices: ML, deep learning, generative AI, LLMs; Design robust, scalable AI solutions aligned with business objectives; Ensure the technical quality of deliverables and oversee code reviews; Drive technology choices and maintain active monitoring of AI innovations; Establish engineering best practices (testing, code review, standards); Lead and develop a team of AI Engineers (junior to senior profiles); Structure AI engineering practices: code standards, MLOps, documentation; Contribute to the recruitment and career development of AI profiles within Keyrus; Run technical workshops and share knowledge within the practice; Manage end-to-end production deployment of AI use cases (from POC to production); Design and implement advanced RAG architectures (ingestion, indexing, generation); Optimize GenAI systems (prompt engineering, fine-tuning, evaluation); Implement data pipelines and model monitoring systems; Interact directly with clients: scoping, reporting, strategic advisory; Ensure performance, security, and cost optimization of deployed AI solutions.

Requirements

• Minimum 6 years' experience in AI engineering/data science, including at least 2 years in technical leadership
• Degree in Computer Science, Data Science, Applied Mathematics or equivalent
• Proven expertise with LLMs, generative AI and RAG architectures
• Strong proficiency in Python and the ML ecosystem (PyTorch, HuggingFace, LangChain, etc.)
• Hands-on experience in MLOps and production deployment: CI/CD, orchestration, monitoring (MLflow, Airflow, etc.)
• Familiarity with cloud and data platforms (Snowflake, Azure, AWS, GCP)
• Ability to communicate complex technical concepts to business stakeholders
• Natural leadership, pedagogical skills, ability to develop a team, autonomy and team spirit
• Professional proficiency in English is a plus

ATS Optimization Keywords

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Hard Skills

• AI architecture
• machine learning
• deep learning
• generative AI
• large language models
• Python
• MLOps
• data pipelines
• prompt engineering
• fine-tuning

Soft Skills

• leadership
• team development
• communication
• autonomy
• team spirit
• pedagogical skills
• strategic advisory
• technical workshops
• client interaction
• code review