Job Description

Job Description
Seeking an AI Engineer to design, develop and deploy applications and services powered by artificial intelligence, with a strong focus on Machine Learning and Generative AI use cases. The candidate will work on the creation and integration of solutions based on large language models, ensuring reliability, scalability and performance, in addition to contributing to the advancement of AI initiatives through collaboration with different technical and product areas.
Responsibilities:
Design, develop and implement AI-powered applications and services, with special focus on Machine Learning and Generative AI.
Build and integrate LLM-based solutions using frameworks such as LangChain, ensuring reliability, scalability and performance.
Collaborate with product managers, designers and data teams to translate business requirements into technical AI solutions.
Implement data pipelines, model training flows and inference services in different environments.
Optimize models and systems for performance, costs and scalability in production.
Ensure the adoption of good practices related to model evaluation, monitoring, versioning and responsible use of AI.
Contribute to technical discussions, architectural decisions and proofs of concept for AI initiatives.
Stay up to date with emerging AI technologies, tools and industry trends.
Requirements:
Experience as an AI Engineer, Machine Learning Engineer or similar position.
Knowledge of Machine Learning concepts, algorithms and model life cycle.
Practical experience with Generative AI, including LLMs and prompt engineering.
Experience using LangChain to build and orchestrate LLM based applications.
Proficient in Python and common AI/ML libraries and frameworks (e.g. TensorFlow, PyTorch, Scikit-learn).
Experience deploying AI solutions in cloud environments (AWS, Azure or GCP).
Understanding of good software engineering practices, including version control, testing, and CI/CD.
Advanced English (spoken and written), with the ability to communicate effectively with technical and non-technical stakeholders.
Desirable:
Experience in MLOps practices, including model monitoring, retraining, and automation.
Familiarity with vector databases, embeddings and recovery augmented generation (RAG) patterns.
Knowledge of data engineering concepts and tools.
Experience working in agile environments and multidisciplinary teams.
Knowledge of AI ethics, privacy, and security best practices.
Previous experience in consulting or projects with direct interaction with clients.

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