Halliburton
Brazil- Remote: Senior Backend Engineer (Python) (Rio de Janeiro, RJ, BR, 29194
Remote Backend Engineer role with clear candidate location fit.
PostedJul 23, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Brazil
Role overview
Brazil- Remote: Senior Backend Engineer (Python) (Rio de Janeiro, RJ, BR, 29194
Requirements and responsibilities
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Details
- 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.
Core Responsibilities
- 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.
Streaming & High-Throughput Data
- 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.
GenAI & Autonomous Agents
- 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
Graph & Knowledge Engineering
- 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.
The Technology Stack
- Core Backend: Python 3.12+ (FastAPI, Pydantic), Polars, Docker, Kubernetes.
- Streaming: Apache Kafka, Flink, Spark Streaming, TimescaleDB, Tiger Data.
- GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs.
Must Haves:
- 5+ years of experience in Python Backend development.
- Advanced English communication skills.
- Computer Science fundamentals: Data Structures and Algorithms.
- Strong experience building and documenting REST APIs (FastAPI).
Good to Have:
- 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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