**AI Engineer - English B2 - Remote Colombia** **Responsibilities**: - Prompt Engineering: Create and optimize prompts to effectively interact with LLMs, improving model outputs and aligning them with business objectives. Experience with prompt optimization libraries like DSPy is a plus - Model Evaluation and Fine-tuning: Evaluate the performance of ML models and LLMs, conducting rigorous testing and validation to ensure they meet quality standards. Fine-tune models based on evaluation results. - Data Preparation and Management: Collect, preprocess, and manage large datasets. Knowledge of data warehouse best practices. - Cross-Functional Collaboration: Collaborate with software engineers, data scientists, product managers, and other stakeholders to integrate AI models into products and services. - Research and Innovation: Stay updated on the latest developments in Generative AI, LLMs, and machine learning frameworks to incorporate best practices and new technologies into our solutions. - Deployment and Monitoring: Deploy models into production environments and monitor their performance, making necessary adjustments and improvements over time. **Qualifications** - Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - English B2 or above - Experience: 5+ years of experience as an AI/ML Engineer or in a similar role. - Proficiency with LLMs: Hands-on experience with LLMs using OpenAI GPT 4/4o/o1, Anthropic's Claude, Google's Gemini, or open-source models like LLaMa. Great understanding of how to build RAG systems, agent frameworks, and LLM chains. - Prompt Engineering Skills: Demonstrated ability to craft and optimize prompts to guide LLM outputs effectively. - Model Evaluation Expertise: Experience in evaluating ML models and LLMs using appropriate metrics and methodologies. - Programming Skills: Proficiency in Python and experience with machine learning frameworks such as PyTorch. - Machine Learning Expertise: Solid understanding of machine learning algorithms, deep learning techniques, and natural language processing. You should know when to use XGBoost vs an LLM. - Data Engineering Expertise: Solid understanding of SQL (Postgres) and Data Warehouse management (Snowflake) - Problem-Solving Skills: Strong analytical skills with the ability to tackle complex challenges. - Communication: Excellent verbal and written communication skills for effective collaboration. - Team Player: Ability to work collaboratively in a multidisciplinary environment. **Preferred Qualifications** - Cloud Experience: Familiarity with deploying models on cloud platforms like AWS, Google Cloud, or Azure. - Open-Source Contributions: Experience contributing to open-source AI projects or communities. - Big Data Technologies: Knowledge of Hadoop, Spark, or other big data processing frameworks. - Domain Knowledge: Experience in industries such as finance, healthcare, retail, or technology. Tipo de puesto: Tiempo completo