3+ years designing, building, and supporting production ML or LLM systems used by real users; POCs, demos, and coursework alone are not sufficient.
GenAI/LLM engineering: RAG, chunking, hybrid search, reranking, embeddings, vector search, tool/function calling, structured outputs, and handling incorrect or unsupported answers.
Agentic AI: agents or multi-step automation using LangGraph, LangChain, Semantic Kernel, or similar, including tools, guardrails, human approval, and action auditing.
Evaluation: defined evaluation sets and metrics, measured accuracy or error rates, and system improvements based on results.
AIOps/anomaly detection: time-series anomaly detection and alert-noise reduction across metrics, logs, or events.




Python production code with tests; relevant libraries may include Pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
Production troubleshooting during live incidents across Linux (CPU, memory, disk, network), Java (heap, garbage collection, thread dumps), and Python.
Observability: Splunk (SPL, dashboards, alerts) plus at least one of Prometheus, OpenTelemetry, Dynatrace, or a similar tool.
RCA/AIOps tooling: significant contribution to root-cause analysis, AIOps, or incident-analysis tooling.

#J-18808-Ljbffr

📌 Artificial Intelligence Engineer (Ciudad de México)
🏢 Bull-It Solutions
📍 Ciudad de México