Full-Stack AI Developer

Here is a brief overview of the tasks and responsibilities:

• Contribute to the deployment and industrialization of AI solutions by putting AI/ML models and solutions into production in reliable, reproducible, secure and scalable environments;
• Develop and maintain MLOps pipelines required for the AI solution lifecycle, including integration, testing, deployment, versioning, and monitoring;
• Design, deploy, and maintain cloud infrastructures using Infrastructure as Code (IaC) approaches to ensure reproducibility and automation of environments;
• Deploy and operate AI applications, services, and components in containerized environments;
• Design, implement, and optimize ETL/ELT pipelines for structured and unstructured data using modern data engineering tools and frameworks;
• Integrate data from multiple sources, including internal systems, APIs, and external datasets, into centralized data platforms such as data lakes and data warehouses;
• Develop software components, APIs, scripts and services to integrate Data/AI solutions with customers' existing systems;
• Develop and maintain logical and physical data models that meet analytics, AI/ML, and reporting needs;
• Optimize the performance, scalability, and reliability of data systems, including query optimization and resource management;
• Implement data validation, cleansing, and monitoring processes to ensure accuracy, consistency, and compliance with governance frameworks;
• Implement continuous integration and deployment (CI/CD) mechanisms for data pipelines, software components, and AI solutions;
• Contribute to the monitoring, diagnosis and continuous improvement of solutions deployed in production;
• Contribute to ensuring the security and confidentiality of the data and solutions deployed as well as compliance with regulatory requirements;
• Maintain clear documentation of data pipelines, architectures, deployments, and processes;
• Collaborate closely with data scientists and AI engineers to transform prototypes and proofs of concept into robust, production-ready solutions;
• Adapt to the needs of different projects and quickly explore, evaluate and appropriate new technologies when necessary;
• Stay on top of trends in data engineering, MLOps, cloud technologies, and AI industrialization practices;
• Actively contribute to the continuous improvement of internal processes.

The profile we are looking for is as follows:

• Hold an undergraduate degree in software engineering, computer science, or a related field;
• Accumulate three (3) to five (5) years of relevant professional experience in data engineering, software development, MLOps, DevOps, or AI/ML deployment, ideally in several different projects, technologies, or contexts;
• Demonstrate versatility in different dimensions of solution engineering, including software development, data, cloud, infrastructure, automation, or AI/ML;
• Have deployed and operationalized solutions in production environments;
• Master the principles of Infrastructure as Code (IaC), especially with Terraform, Ansible or equivalent tools;
• Understand MLOps practices and tools as well as the full lifecycle of an ML solution, from development to deployment and monitoring;
• Master CI/CD practices and tools as well as the automation of deployment processes;
• Strong software development skills, including designing APIs, services, scripts, and integrations needed to operationalize solutions;
• Understand the principles related to computer networking and security in cloud environments;
• Have a good knowledge of machine learning and ML concepts in order to collaborate effectively with data scientists and contribute to the industrialization of their models;
• Quickly deepen your knowledge and appropriate new technologies according to the needs of the projects;
• Master Python at an advanced level;
• Move with ease in the Microsoft environment;
• Experience with cloud platforms, including AWS or Azure;
• knowledge of GCP is an asset;
• Proficiency in Linux, Docker, and containerized environments;
• experience with Kubernetes or cloud orchestration services is a strong asset.