Sr. Machine Learning Engineer - Search

About the role

An experienced Senior Machine Learning Engineer is sought to build challenging, high-impact ML and AI systems. The engineer takes ownership of the full ML lifecycle, from experimentation and model development to deployment and monitoring in production. The role sits at the intersection of classical signal processing, deep learning and modern LLM-powered systems, and is based in Montevideo with a hybrid setup.

Key responsibilities

• Design, develop and deploy machine learning models and AI-powered features into production systems used at scale.
• Build and maintain end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation and serving.
• Work with LLMs on real-world problems, including prompt engineering, RAG architectures, LLM observability and integration with orchestration frameworks such as LangGraph, PydanticAI or OpenAI Agents.
• Collaborate with backend and data engineering teams to integrate ML solutions into existing infrastructure.
• Monitor and maintain models in production, ensuring reliability, performance and fairness over time.
• Contribute to MLOps practices: experiment tracking, model versioning, CI/CD for ML and observability.

Required profile

• 3+ years of professional experience in Machine Learning or AI engineering roles.
• Hands-on experience building and shipping ML systems in production.
• Practical experience with LLMs, whether through fine-tuning, RAG or building LLM-powered applications.
• Experience with cloud platforms and ML tooling; AWS preferred.
• Experience with containerization (Docker) and software engineering best practices (testing, versioning, CI/CD).
• Strong communication skills and advanced written and spoken English, working directly with English-speaking stakeholders.

Required skills

• Supervised and unsupervised learning, model evaluation and feature engineering.
• Proficiency in Python and at least one deep learning framework (PyTorch preferred).
• Building and consuming APIs with FastAPI, Flask or Django.
• Nice to have: vector databases (Qdrant, Chroma, OpenSearch, pgvector) and semantic search, NLP, information retrieval or recommendation systems background, familiarity with model evaluation frameworks and responsible AI practices.