FEMSA
From FEMSA's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Objective We’re building an AI-first organization — and we’re looking for engineers who actually build, not just experiment.
Objective We’re building an AI-first organization — and we’re looking for engineers who actually build, not just experiment. We’re hiring an AI Engineer to develop, integrate, and maintain AI-powered products across our ecosystem — from LLM-based customer experiences to internal copilots that transform how teams operate.
You’ll be part of a centralized AI Products Chapter, embedding into squads to bring real AI solutions into production.
Build and deploy Generative AI solutions (LLMs, RAG, agents, copilots) under standard production practices.
Implement scalable AI components and services within production systems.
Work across the full software lifecycle: assist in prototyping, deploy models, and optimize performance.
Integrate AI models with real-world systems, APIs, data platforms, and backend workflows.
Implement evaluation frameworks, monitoring mechanisms, and feedback loops to track model performance.
Partner with Product, Data, and Engineering squads to deliver impactful AI-driven features.
Solid software engineering background (Python + modern backend stack).
Hands-on experience building with LLMs, Generative AI, or NLP frameworks.
Practical experience deploying ML/AI features into production environments.
Familiarity with cloud services (AWS/GCP), API design, and data pipelines.
A strong problem-solving, execution-oriented, and continuous-learning mindset.
You won’t just build models → you’ll ship real AI products into production.
You’ll collaborate across multiple squads → gain broad exposure and technical growth.
You’ll contribute directly to shaping best practices for AI Engineering within the organization.
Required Knowledge & Experience
Experience: 3–5 years of overall experience in Data & Tech, with at least 1.5–2 years of hands-on experience developing AI/ML and Generative AI solutions.
Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Applied Mathematics, or a related technical field.
AI Competencies: Strong foundational understanding of Deep Learning, Natural Language Processing (NLP), and Generative AI (LLMs, RAG architecture, function calling, agents).
Technical Stack: Proficiency in Python and familiarity with ML libraries (PyTorch or TensorFlow), alongside hands-on use of AI frameworks (LangChain, LlamaIndex, or Hugging Face).
Cloud & Data Platforms: Hands-on experience with cloud-native AI/ML services (AWS SageMaker or Google Vertex AI) and standard data processing tools.
Infrastructure & Tools: Practical experience using vector databases (Pinecone, Milvus, Qdrant, or Weaviate) and MLOps tools for model tracking and logging.
Communication: Ability to clearly explain technical trade-offs and AI concepts to both technical teammates and product partners.
Ethics & Compliance: Basic understanding of AI safety, data privacy, and responsible AI practices.
Soft Skills: Proactive problem-solving, strong sense of ownership, willingness to learn, and commitment to writing clean, maintainable code.
If you’re excited about building real AI products (not just talking about them), we should talk. 🚀
En Spin estamos comprometidos con construir un lugar de trabajo diverso e inclusivo.
Creemos en la igualdad de oportunidades y promovemos un entorno libre de discriminación por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad o cualquier otra condición legalmente protegida.
Scores your resume against this job the way resume scanners do: the job's hard and soft skills, searchability, recruiter tips and formatting.
Create a free account to see your score. Your resume stays in this browser until then, and we never store it.
Already a member? Sign in