Collaborate with the AI Solutions Architect to translate business requirements into scalable, efficient ML/AI models and pipelines.
Implement AI models, validate performance, and ensure integration within existing architectures.
Develop robust data pipelines: data collection, processing, model training, and model deployment.
Integrate AI solutions into production environments, following MLOps best practices (monitoring, automation, scalability; familiarity with MLflow or platforms such as Azure ML or Google Vertex AI).
Work closely with commercial and product teams to prototype and validate proposals, delivering iteratively and focusing on customer-centered solutions.
Actively participate in assessing technical risks (performance, scalability, security) and collaborate on implementing preventative solutions.
Ensure efficient use of cloud tools (AWS, Azure, GCP) in solution development and deployment.
Contribute to the creation of technical documentation and best practices in alignment with the architect and other stakeholders.

Experience in developing and deploying Machine Learning and Deep Learning models.
Proficiency in programming languages such as Python (TensorFlow, PyTorch, scikit-learn, Pandas).
Experience with MLOps and CI/CD tools (MLflow, Kubeflow, Jenkins, GitLab CI).
Strong experience with SQL and NoSQL databases and query optimization practices.
Experience with cloud computing services (AWS, Azure, or GCP) and container technologies (Docker, Kubernetes).
Familiarity with system architectures, data pipelines, and best practices for API integration (REST, GraphQL).
Experience with Generative AI and large language models (LLMs) (e.g., GPT, BERT).
Experience with cloud orchestration tools such as Airflow, Data Factory, Databricks pipelines.
Experience with Docker, Docker Compose, Kubernetes, or related tools such as Azure Container Apps.
Experience working in agile environments (Scrum, Kanban).
Experience collaborating on projects with solution architects or commercial teams.

AI Engineer

Key skills