Machine Learning Engineer (AI-Powered Risk Intelligence Platform) - Mexico (Contract)

Key Responsibilities

• Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative/topic detection
• Build and improve LLM-powered features, including prompt design, retrieval-augmented generation (RAG), and agentic workflows
• Develop embedding and semantic-search pipelines to group related content and surface emerging threats
• Productionize models as scalable, monitored services with clear latency, cost, and accuracy targets
• Build evaluation frameworks and datasets to measure model quality, bias, and drift over time
• Work with data engineering to improve ingestion, labeling, and feature pipelines for high-volume, noisy, multilingual text data
• Monitor and retrain models in production, and debug quality issues end-to-end
• Join daily standups and technical discussions with the US-based client team

Requirements

• 4+ years of professional experience building and deploying machine learning models in production
• Strong Python skills and hands-on experience with PyTorch, TensorFlow, or Hugging Face Transformers
• Practical NLP experience, such as classification, named entity recognition (NER), topic modeling, or semantic similarity on real-world text data
• Hands-on experience building with LLMs (OpenAI, Anthropic, or open-source models), including prompt engineering and RAG
• Experience with embeddings and vector search (e.g., pgvector, Pinecone, OpenSearch, or FAISS)
• Experience deploying and operating ML services on AWS, GCP, or Azure, using Docker and CI/CD
• Solid grasp of model evaluation, including metrics, test sets, error analysis, and production monitoring

Nice to Have

• Experience with trust and safety, threat intelligence, disinformation research, or social media data
• Experience building agentic AI systems or tool-using LLM workflows (e.g., LangChain, LlamaIndex, or custom frameworks)
• Multilingual NLP experience, especially Spanish-language text
• Familiarity with MLOps tooling such as MLflow, SageMaker, Vertex AI, or Weights & Biases
• Experience with streaming or large-scale data processing (e.g., Kafka, Spark, or Airflow)
• Previous experience working with US-based clients or distributed teams