AI Engineer
About This Vacancy In the role of AI Engineer, you will collaborate directly with technical teams and business stakeholders to understand problems and processes, identify the most effective solutions, and support their entire lifecycle, from the discovery phase to deployment and production implementation.
Your goal will be to rapidly integrate AI technologies into existing ecosystems and applications, reusing components and building blocks when available. Alternatively, you will develop services, integrations, and customized AI agents when necessary.
In Particular, You Will
- Analyze business needs and processes and translate them into production-ready AI solutions.
- Design and develop applications and services based on AI, Machine Learning, and Generative AI, from prototyping to production.
- Integrate LLM and AI Agents with applications, APIs,
data sources, and existing enterprise systems.
- Reuse and extend components and AI building blocks, developing custom integrations or components when necessary.
- Work across the technology stack, including backend, frontend, data/ML, and operations.
- Implement DevOps and MLOps practices, managing CI/CD, containerization, and orchestration.
- Ensure the security, reliability, scalability, testing, and observability of AI applications in production environments.
- Document developed solutions and contribute to the creation of reusable patterns and components to accelerate the development of new projects.
Your goal will be to rapidly integrate AI technologies into existing ecosystems and applications, reusing components and building blocks when available. Alternatively, you will develop services, integrations, and customized AI agents when necessary.
In Particular, You Will
- Analyze business needs and processes and translate them into production-ready AI solutions.
- Design and develop applications and services based on AI, Machine Learning, and Generative AI, from prototyping to production.
- Integrate LLM and AI Agents with applications, APIs,
data sources, and existing enterprise systems.
- Reuse and extend components and AI building blocks, developing custom integrations or components when necessary.
- Work across the technology stack, including backend, frontend, data/ML, and operations.
- Implement DevOps and MLOps practices, managing CI/CD, containerization, and orchestration.
- Ensure the security, reliability, scalability, testing, and observability of AI applications in production environments.
- Document developed solutions and contribute to the creation of reusable patterns and components to accelerate the development of new projects.