Halliburton

Brazil- Remote: Senior Backend Engineer (Python) (Rio de Janeiro, RJ, BR, 29194

Rol remoto de Backend Engineer con fit claro de ubicación del candidato.

Publicado23 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Brasil

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

Ingestion: Handling massive streams of sensor data with minimal latency.
Intelligence: Making LLMs deterministic and reliable within robust multi-agent systems.
Context: Modeling complex ontologies to map thousands of physical assets.
Streaming: Apache Kafka, Flink, Spark Streaming, TimescaleDB, Tiger Data.
GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs.
Problem Solver: You dig into logs to find the root cause of a data spike, a silent failure, or a disconnected node in a graph.
Modern Pythonista: You are up-to-date with modern Python async patterns and typing, ensuring code is high-performance and maintainable.
You understand Big O notation and how to optimize code for CPU/memory efficiency.

High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
System Reliability: Debug complex concurrency issues and ensure production reliability in distributed systems.
Rapid Delivery: Adopt a "deliver fast" mentality without compromising on code quality, testing, or API design standards.

Build Streaming Pipelines: Design scalable services using Kafka and Spark/Flink to process raw sensor data in real-time.
Time-Series Optimization: Optimize database schemas (TimescaleDB) to enable fast historical data retrieval and implement algorithmic checks to validate sensor readings.

Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers

Knowledge Graph Engineering: Design domain ontologies in Neo4j, defining relationships between assets, documents, and time-series data.
Search Infrastructure: Implement Hybrid Retrieval logic combining Vector Search, Full-Text Search, and Graph traversal.

Streaming: Proficiency with Streaming Technologies (Kafka, Flink, Spark) and Time-Series Databases (TimescaleDB, InfluxDB).
GenAI: Practical experience building applications with LLMs, Agentic Frameworks (LangGraph), and Vector Databases.
Graph: Hands-on experience with Graph Databases (Neo4j/Cypher), SQL database design, and Hybrid Search strategies
Background in Heavy Industry, O&G, or IoT data (MQTT, OPC UA).
Experience with Local LLMs (Ollama) for privacy-focused deployments.
Familiarity with Data Lineage or Metadata management.

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