Machine Learning Engineer

We are seeking a Machine Learning Engineer with over 5 years of experience to join our team. The successful candidate will specialize in collecting, processing, and analyzing textual data from various web sources to drive insights that inform business strategies.

You are someone who wants to influence your own development. You’re looking for a company where you have the opportunity to pursue your interests and be able to grow professionally.

You bring to Applaudo the following competencies:

• Minimum +5 years of experience as a Machine Learning Engineer.
• Strong programming skills in Python.
• Experience with data pipelines y MLOps Deployments
• Proficient in using AWS Bedrock and related AWS services for deploying and managing ML models.
• Must: Experience with NLP libraries such as NLTK, spaCy, and transformers.
• Must: Expertise in applying RAG for enhancing model capabilities in real-time decision-making processes.
• In-depth knowledge of data structures, data modeling, and software architecture.
• Advanced understanding of mathematics, statistics, and algorithms.
• Proven track record of working on NLP projects and applying natural language processing in a professional setting.
• Advanced english level, as you'll be working directly with US clients.
• Desirable: Strong foundation in MLOps practices, including automation of model lifecycle, continuous integration/continuous deployment (CI/CD) of ML models, and monitoring model performance.
• Desirable: Highly-skilled in deploying tools like VCS (git) and collaboration (Docker and Kubernetes)
• Desirable: Ability to work in Agile environments and proficiency with project management and collaboration tools.

You will be accountable for the following responsibilities:

• Develop and implement machine learning models to analyze large datasets of unstructured text, such as user reviews and news articles.
• Utilize advanced NLP techniques and ML frameworks to extract meaningful information and trends from textual data.
• Implement and manage RAG and other relevant techniques, knowing when to apply each method for optimal results.
• Collaborate with cross-functional teams to translate data insights into actionable business outcomes.
• Design and maintain robust MLOps pipelines to streamline model training, deployment, and monitoring.