We are looking for a senior professional to work comprehensively in data, machine learning, and applied artificial intelligence.

This role combines Data Engineering, ML, and AI competencies into a single strategic position, with end-to-end involvement from data ingestion to the deployment of models and agents in production.

Responsibilities: — Building and evolving data pipelines with integration of multiple sources — Structuring scalable environments and defining best practices for architecture — Developing predictive models with Python and frameworks such as XGBoost, LightGBM, and CatBoost — Implementing MLOps with MLflow, including versioning, tracking, and continuous monitoring — Developing AI agents and multi-agent systems (LLMs) with LangChain, LangGraph, CrewAI, and n8n — Building RAG architectures with embeddings and vector databases — Integrating AI with real-world systems via REST APIs, Webhooks, and cloud deployment — Ensuring data quality, governance, security, and compliance — Monitoring drift, business metrics, cost, and performance in production — Documenting architecture, workflows, and integrations.

Requirements: — Solid experience with Python (pandas, numpy, scikit-learn, TensorFlow, PyTorch) — Proficiency in advanced SQL and relational and non-relational databases — Experience with cloud platforms: AWS, Azure, or GCP — Familiarity with modern data platforms: Databricks, Snowflake, Data Lakes, Lakehouse — Mastery of ETL/ELT with Airflow and dbt — Experience with LLMs, autonomous agents, and RAG architectures in production — Knowledge in statistics, probability, and linear algebra.