AI Engineer - LATAM

DUTIES & RESPONSIBILITIES 

• Architect and develop solutions utilizing Large Language Models and/or ML algorithms 

• Implement AI and Agentic workflows utilizing libraries such as LangChain and LangGraph, or llama-index 

• Develop proof of concepts for innovative AI applications and lead learning efforts to adopt new AI techniques, focusing on building user-centric products that align with the overall product vision. 

• Perform high-impact evaluation of LLM-generated output, including but not limited to relevancy, usefulness, correctness, precision/recall, among other criteria. 

• Write clean, efficient, and well-documented Python code, utilizing frameworks and libraries like NumPy, Pandas, and Matplotlib. 

• Apply a strong understanding of NLP for tasks such as text preprocessing, sentiment analysis, clustering, and language generation models. 

• Ensure AI systems' security and conduct rigorous model evaluations to maintain robustness and reliability. 

• Work as part of an Agile delivery team bringing AI-centric projects from idea conception to execution, ensuring timely delivery within designated timeframes. 

• Commit to continuous learning to keep abreast with the latest AI and machine learning technological advancements, applying these to practical applications for business solutions. 

EXPERIENCE 

• Minimum of 4 years of professional experience in ML/AI, with substantial work focusing on the practical application of these technologies. 

• At least 1 year of focused experience with Large and/or Small Language models, closed or open-source.  

• Proven track record of developing and deploying AI-driven applications into production, understanding the full lifecycle from development to deployment and maintenance. 

• Advanced Python skills, with experience using deep-learning frameworks such as TensorFlow, PyTorch, and familiarity with NLP frameworks. 

• Experience in agile development and enterprise SDLC, and the ability to deliver complex projects on schedule. 

• Experience in full-stack development is an advantage, but not required 

COMPETENCIES/SKILLS 

• Extensive knowledge of generative AI concepts and techniques, Large Language Models, and how to implement them. 

• Demonstrable knowledge of: 

• Agentic Workflows and advanced RAG techniques. 

• Utilization of libraries such as Langchain and Langgraph for language model integration 

• Function Calling 

• Fine-Tuning vs. Prompt Engineering 

• Evaluation 

• Advanced proficiency in Python and familiarity with relevant frameworks and libraries (e.g., NumPy, Pandas, Matplotlib). 

• Strong fundamentals in NLP techniques and applications. 

• Solid understanding of software development practices, including version control with Git, and full-stack development capabilities. 

• Deep aptitude for problem-solving and analytical skills. 

• Excellent communication skills for effective collaboration and knowledge dissemination. 

• Commitment to continuous learning and staying updated on industry and technology trends. 

CERTIFICATIONS/LICENSES 

Add certifications/licenses (if any) here. 

• Relevant certifications in machine learning, AI, or data science are highly regarded but not mandatory.
• Certifications like the TensorFlow Developer Certificate or AWS Certified Machine Learning - Specialty are examples of desirable qualifications.