Ingeniero especialista en inteligencia artificial

Design, develop, and deploy artificial intelligence and machine learning solutions to support business processes and product innovation. Lead AI model lifecycle management, including data collection, model training, validation, deployment, and continuous improvement. Develop internal frameworks, guidelines, and best practices to ensure responsible and effective use of AI technologies across the organization.

Education

Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or Software Engineering.

Skills & Qualifications

• Ability to work in a team-based environment and a commitment to continuous learning and improvement.
• 3+ years of experience in AI/ML development and deployment: Python, TensorFlow, PyTorch, Scikit-learn, Keras.
• Experience with MLOps & Cloud tools: MLflow, Kubeflow, Azure ML, AWS SageMaker.
• Experience in NLP & Computer Vision: Hugging Face Transformers, OpenCV, YOLO, spaCy.
• Data engineering skills: Apache Spark, SQL, Pandas, dbt.
• Experience with experiment tracking and version control for AI: Git, DVC, GitHub, GitLab.
• Knowledge of MLflow, Weights & Biases for experiment tracking and model registry.
• Experience with Docker & Kubernetes for containerization and orchestration of AI model serving.
• Deep knowledge of machine learning algorithms, model architectures, and AI system design.
• Knowledge of statistical modeling, data analysis, and hypothesis testing.
• Experience with large language models (LLMs), generative AI, and prompt engineering.
• Knowledge of responsible AI, model explainability (XAI), and fairness frameworks.
• Ability to interpret and communicate AI model results to both technical and non-technical audiences.
• Knowledge of data governance, data lineage, and AI traceability systems such as feature stores and model registries.
• Proficiency in cloud platforms: Azure, AWS, or GCP for AI/ML workloads and deployment.
• Knowledge of API design, microservices architecture, and AI model integration with enterprise systems.
• Deep knowledge of AI model evaluation metrics, A/B testing, and statistical performance analysis.