Sr. Python Developer – Generative AI & MLOps Specialist (French/English)

Overview

About the Role:
Join a forward-thinking team working on cutting-edge generative AI and large language models (LLMs). This role involves developing Python-based AI tools, managing CI/CD pipelines, and integrating AI models into production environments. The candidate will work closely with multidisciplinary teams, communicating AI insights effectively in both French and English.

Key Responsibilities:

• Design, develop, and maintain Python applications related to generative AI, data pipelines, and automation workflows.
• Develop and deploy LLM-based AI models using AWS Bedrock, OpenAI API, Anthropic Claude, Llama 3, or Mistral.
• Implement and manage CI/CD pipelines, infrastructure as code (Terraform), and containerized deployments (Docker, ECS).
• Conduct testing, validation, and iteration from Proof of Concept (POC) through to MVP and full production.
• Collaborate with cross-functional teams to explain AI design decisions and project progress clearly.
• Support observability and quality assurance via MLflow, Weights & Biases, Evidently, and SonarQube.
• Manage MLOps/LLMOps pipelines including dataset curation and light retraining activities.

Required Skills & Experience:

• 5–10 years professional software development experience, primarily with Python.
• 1–2 years hands-on experience with generative AI/copilot/code assistant projects and LLMs.
• Practical knowledge of LLM limitations such as hallucinations and deterministic prompting.
• Strong experience with DevOps practices, CI/CD pipelines, and integrating models into production stacks.
• Familiarity with LangChain, LlamaIndex, or DSPy for prompt orchestration and tool use.
• Proficient in AWS cloud services (Bedrock, Lambda, S3, ECS), Docker, Terraform.
• Good communicator fluent in both French and English, able to explain complex AI topics to non-experts.
• Scientific and structured mindset with emphasis on testing, measuring, and iterating AI models.

Preferred Qualifications:

• Experience in sensitive sectors like fintech, compliance, or data security.
• Contributions to open-source LLM or AI infrastructure projects.
• Experience with internal AI orchestration frameworks or automated codebase maintenance.