Core requirements

· Production AI ML around 3+ years designing building and supporting ML or LLM systems used by real users in production. POCs demos and coursework alone are not sufficient.

· GenAI/LLM engineering: RAG (chunking hybrid search re ranking) embeddings and vector search tool/function calling structured outputs and handling of incorrect or unsupported answers.

· Agentic AI agents or multi-step automations with LangGraph LangChain Semantic Kernel or similar including tools guardrails human approval and audit of actions.

· Evaluation defined evaluation sets and metrics measured accuracy or error rates and improved a system based on the results.

· AIOps / anomaly detection: time-series anomaly detection and alert-noise reduction on metrics logs or events.

· Python production code with tests (Pandas NumPy scikit learn PyTorch or TensorFlow where relevant).

· Production troubleshooting: live incidents on Linux (CPU memory disk network) Java (heap GC thread dumps) and Python.

· Observability tools Splunk (SPL dashboards alerts) plus one of Prometheus OpenTelemetry Dynatrace or similar.

· RCA/AIOps tooling has built or contributed significantly to RCA AIOps or incident-analysis tooling.

📌 Artificial Intelligence Engineer (Guadalajara)
🏢 BULLS SYSTEM
📍 Guadalajara