- Company Name
- Theodo
- Job Title
- Lead AI Engineer - CDI Paris - Theodo Data & AI
- Job Description
-
**Job Title**
Lead AI Engineer – CDI
**Role Summary**
Lead the design, development, and deployment of high‑quality AI solutions for diverse industry clients (health, fintech, retail, public sector). Oversee a small team of AI engineers, drive technical excellence, ensure timely delivery, and champion continuous improvement within a Lean‑centric environment.
**Expectations**
- Own end‑to‑end AI product lifecycle: concept, architecture, training, deployment, and production monitoring.
- Deliver top‑quality solutions on schedule and maintain performance standards.
- Coach and develop 1‑3 junior AI engineers.
- Proactively propose technical initiatives and share knowledge internally and externally (conferences, publications).
**Key Responsibilities**
- Architect and implement AI projects (traditional tasks like object detection and sales forecasting; generative tasks such as Retrieval‑Augmented Generation, AI agents).
- Collaborate with Product Manager and Engineering Manager to define scope, objectives, and success metrics.
- Lead code reviews, enforce coding standards, and adopt best practices for reproducibility and scalability.
- Manage model lifecycle: data preparation, feature engineering, hyper‑parameter tuning, model validation, and deployment to cloud platforms.
- Develop monitoring pipelines, set up alerting, and troubleshoot issues in production.
- Stay current with AI research, hyper‑scaler services (AWS, GCP, Azure), and emerging frameworks.
- Engage in cross‑functional initiatives, including data engineering phases when client needs shift to data‑centric solutions.
- Mentor team members and contribute to technical community activities (guilds, workshops).
**Required Skills**
- Strong experience in AI/ML development (Python, PyTorch/TensorFlow, HuggingFace, OR similar).
- Proficiency with cloud AI services (AWS SageMaker, GCP Vertex AI, Azure ML).
- Experience with data pipelines, ETL, and feature engineering tools (Airflow, dbt, Spark).
- Knowledge of traditional ML techniques and generative AI models (LLMs, RAG, multimodal).
- Familiarity with Docker/Kubernetes and CI/CD for model deployment.
- Solid understanding of version control (Git), unit testing, and code quality tools.
- Excellent communication, teamwork, and mentoring abilities.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or related field.
- Relevant certifications in cloud platforms (AWS Certified Machine Learning – Specialty, GCP Professional Machine Learning Engineer, Azure AI Engineer Associate) are a plus.