• Develop and use modern software engineering practices to deploy ML solutions at scale, including building CI/CD pipelines and automated testing. • Work with Data Scientists and Data Engineers to build automated pipelines that train, run and monitor ML Models for business applications in an agile and elegantly orchestrated manner • Enhance and improve the code deployment and model monitoring frameworks and project operations documentation • Support life cycle management of deployed ML model life cycle management (e.g. new releases, change management, monitoring and troubleshooting) • Support the MLOps Platform, including model registry, model deployment, and feature store. • Collaborate with expert vendors and IT application teams for integrating ML models including defining SLAs and designing highly automated end-to-end testing

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental. Job Description: Machine Learning Engineer We are looking for an experienced and driven Machine Learning Engineer to join our Advanced Analytics team. You will play a pivotal role in advancing our machine learning capabilities, focusing on the building, training, deploying, scoring, and monitoring of models for various use cases, including personalized recommenders, forecasting, and LLM modeling. Responsibilities - Model Development & Deployment: Develop and deploy Machine Learning, Deep Learning, and GenAI models to enhance operational efficiency and customer experience. - Continuous Improvement: Improve models by monitoring performance, conducting A/B testing, and implementing feedback loops. - Architecture & Scalability: Architect and rebuild complex ML frameworks from the ground up, incorporating multi-threaded processing and distributed workloads to support scalable pipelines. - Cross-functional Collaboration: Work with engineers and product managers to integrate AI solutions into production systems within a fast-paced Agile environment. - Operational Excellence: Own production ML systems, participating in on-call rotations, troubleshooting incidents, and maintaining overall model reliability. - Innovation: Stay updated with the latest advancements in AI to ensure our solutions remain cutting-edge. Requirements - Education: Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field. - Experience: 3+ years of experience designing, building, and operating production-scale machine learning systems. - Technical Mastery: Expert-level programming in Python with extensive experience using PySpark and distributed data platforms (e.g., Databricks) for large-scale datasets. - Deep Learning & Frameworks: Strong experience with Scikit-Learn, MLlib, and PyTorch, including regression, time series, clustering, and deep learning. - Distributed Systems: Deep understanding of Spark execution models, partitioning strategies, shuffle optimization, and vectorized processing (Pandas UDFs). - Data & Cloud: Strong SQL expertise and experience deploying ML workloads to AWS (EC2, S3, DynamoDB, Lambda). - Ops: Experience implementing MLOps / LLMOps practices. Key Competencies - Ownership: Ability to manage strategic initiatives in a rapidly evolving QSR environment. - Adaptability: Eagerness to learn and adapt in ambiguous problem spaces with a collaborative attitude. - Outcome-Oriented: Focus on analyzing and visualizing data to drive continuous improvement across the business. - Professionalism: Maintain transparency and professionalism in communication within the team and with stakeholders. 📍 Location - Flexible / Remote options available.

• Contribute to advancing Reddit's Search and Recommendations products by designing AI-driven Search experience prioritizing seamless and delightful user experience. • Deeply understand the Reddit search product and drive the vision for the search relevance team. • Enhance core search retrieval and ranking, design and implement new search engine features, build and scale search indexes, develop and test new pipeline components. You will also deploy ML models, integrate LLMs, and ensure robust monitoring and smooth product integration throughout the process. • Collaborate across disciplines and with ML, Product, Infrastructure, and DS teams at Reddit to find technical solutions to complex challenges. • Mentor and guide senior and staff engineers in the team. • Influence org-wide technical direction and partner closely with senior leadership to shape the long-term roadmap

• Interpret vague requirements and develop models to solve real-world problems. • Conduct ML experiments using programming languages with machine learning libraries. • Leverage generative AI to develop innovative solutions. • Optimise machine learning solutions for performance and scalability. • Implement tailored machine learning code to meet specific needs. • Ensure efficient data flow between databases and backend systems. • Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. • Create machine learning architectures using Google Cloud tools and services. • Build and deploy production-grade software for machine learning and data-driven solutions.