4 months ago
Mexico City, MexicoMid Level
Responsibilities
- Design and build scalable ML pipelines for unstructured data such as documents, transcripts, images, logs, and free-form text.
- Develop and maintain production-ready AI services and APIs for internal applications and analytics platforms.
- Productionize Generative AI and machine learning solutions through packaging, containerization, deployment, versioning, and lifecycle management.
- Build RAG, embedding, semantic-search, and LLM-powered API workflows.
- Implement monitoring, observability, model-performance tracking, and reliability practices for deployed systems.
- Collaborate with Data Scientists to transition experimental models into stable production environments.
- Contribute to ML CI/CD, automated testing, deployment automation, retraining workflows, and infrastructure standardization.
- Translate business needs into scalable AI-enabled solutions with cross-functional stakeholders.
- Document systems and technical decisions for maintainability and team use.
Requirements
- 3+ years of professional experience in ML Engineering, Applied AI, Data Science, or related engineering roles focused on production systems.
- Hands-on experience with unstructured-data solutions including NLP pipelines, document processing, OCR, text classification, embeddings, entity extraction, or semantic search.
- Proficiency in Python and SQL, with experience using PySpark and Databricks or similar distributed compute platforms.
- Experience developing and deploying production APIs for ML model serving using FastAPI or similar frameworks.
- Familiarity with MLOps practices including ML CI/CD, model monitoring, automated retraining, Docker, and Git-based workflows.
- Experience with cloud-based AI/ML platforms and enterprise deployment environments.
- Strong software engineering fundamentals in readable code, testing, debugging, and performance optimization.
- Preferred experience includes OpenAI, Azure OpenAI, or open-source models; LangChain or LlamaIndex; Azure AI Search, Pinecone, or pgvector; and responsible AI practices such as explainability, bias detection, governance, and model risk management.
Benefits
- Hybrid work arrangement with remote flexibility and at least three days per week in the local office or onsite with clients.
- An inclusive and flexible work environment with collaboration, professional development, and in-office connection benefits.
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