Keep IT Simple

Backend Engineer

• Buscamos uma pessoa desenvolvedora .NET com experiência sólida em construção e evolução de soluções backend;
• Capaz de atuar em um ambiente dinâmico, com demandas contínuas de melhoria, ajustes estruturais e otimização de performance;
• Procuramos alguém que consiga trabalhar bem com integrações, APIs, boas práticas de arquitetura;
• É fundamental que essa pessoa tenha visão técnica apurada, autonomia para tomada de decisão e proatividade para propor soluções escaláveis e sustentáveis.

Desenvolvedor DotNet Sr

• Development and consumption of SOAP and REST APIs;
• Integrations between corporate systems;
• Development of workflows and scheduled tasks;
• Log analysis and handling;
• Troubleshooting and production support;
• Development and maintenance of customizations in Protheus;
• Support and monitoring of integrations;
• Incident analysis and resolution;
• Development of evolutionary enhancements;
• Technical support for testing (homologation) and deployments;
• Work on financial, banking, and operational integrations.

Senior Backend Developer – Protheus

Cloud Engineer

• Design, implement, and maintain scalable and efficient cloud-based solutions on AWS and Azure;
• Lead initiatives to automate cloud infrastructure;
• Collaborate with teams to integrate best practices in development, code quality, and automation;
• Guide and mentor development teams, providing expertise in DevOps and automation practices;
• Contribute to the design and implementation of cloud applications using serverless architectures, Kubernetes, and event-driven patterns;
• Develop and maintain CI/CD pipelines to streamline deployments, utilizing GitOps methodologies;
• Apply security best practices to design and implement secure authentication and authorization mechanisms;
• Monitor and optimize the performance, scalability, and reliability of cloud applications;
• Stay updated with the latest cloud technologies and development trends, applying new tools and frameworks as needed;
• Ensure software systems meet functional and non-functional requirements while adhering to best practices in software design, testing, and security;
• Foster continuous improvement by sharing knowledge, conducting team reviews, and mentoring junior developers.

Senior Cloud Engineer

Threat Intelligence Specialist

• Agent-based hunting: Leverage the power of automation and machine learning models.
• Continuous Compromise Assessments: Develop the capability to perform continuous compromise assessments across all assets.
• Continuous Improvements: Stay at the forefront of cybersecurity trends by continuously updating your threat knowledge.

Cyber Threat Hunter

Software Engineer

• Development and support of code on SSIS and write new code on Python for Klondike teams.

Senior Developer

Data Engineer

• Design, build, and operate the data infrastructure that powers AI and analytics initiatives.
• Build the foundational data layer for LLM applications, RAG systems, and AI-powered products alongside classic data pipelines and analytics infrastructure.
• Own the full data lifecycle: from ingestion and transformation to quality, governance, and serving, with a particular focus on the emerging data patterns required by modern AI systems.
• Build and maintain vector databases and RAG infrastructure, designing high-performance ETL/ELT pipelines, and ensuring data quality at every stage.
• Enable AI engineers, data scientists, and business analysts to build and deploy AI-powered solutions with confidence in the underlying data.
• Design and build scalable, fault-tolerant data pipelines for batch and real-time/streaming workloads;
• Implement modern ELT patterns using dbt, Spark, or Dataflow for transformation within cloud data warehouses;
• Build data ingestion pipelines from diverse sources: APIs, databases, SaaS platforms, file systems, event streams, and document repositories;
• Implement incremental processing, CDC (Change Data Capture), and event-driven pipeline architectures for near-real-time data availability;
• Design pipeline orchestration using Apache Airflow, Prefect, Dagster, or cloud-native workflow services;
• Build and maintain data contracts between producers and consumers to ensure schema stability and backward compatibility.
• Design, deploy, and optimize vector database infrastructure for AI applications: Pinecone, Weaviate, ChromaDB, pgvector, Qdrant, or Milvus;
• Build document ingestion and processing pipelines for RAG: document parsing (PDF, DOCX, HTML, images), chunking strategies (semantic, recursive, sentence-window), and metadata enrichment;
• Implement and optimize embedding generation pipelines using models from OpenAI, Cohere, Voyage AI, or open-source alternatives (BAAI/bge, Nomic);
• Design hybrid search architectures combining dense vector search with sparse retrieval (BM25) and metadata filtering for optimal RAG performance;
• Build and maintain knowledge base management systems: versioned document corpora, incremental indexing, and stale content detection;
• Implement RAG evaluation infrastructure: retrieval accuracy metrics (MRR, NDCG, Hit Rate), context relevance scoring, and end-to-end RAG benchmarks.
• Design and implement comprehensive data quality frameworks: validation rules, anomaly detection, freshness monitoring, and schema enforcement;
• Build data quality pipelines using Great Expectations, Soda, dbt tests, or Monte Carlo for automated data validation at every pipeline stage;
• Implement data lineage tracking and impact analysis across the data platform;
• Design and enforce data governance policies: access control, data classification, PII detection and masking, and retention policies;
• Build data catalogs and discovery tools that enable self-service data access for AI engineers and analysts;
• Monitor and alert on data quality SLAs: completeness, accuracy, timeliness, and consistency.
• Design and maintain the core data platform architecture on cloud-native services (AWS, Azure, GCP) — optimizing for cost, performance, and reliability;
• Build and operate data lake/data lakehouse architectures using Delta Lake, Apache Iceberg, or Apache Hudi on cloud object storage;
• Implement data warehouse solutions using Snowflake, Databricks, BigQuery, or Redshift — with proper partitioning, clustering, and materialization strategies;
• Design data serving layers for diverse consumers: low-latency APIs (feature stores), analytical dashboards, AI model training, and RAG retrieval;
• Implement data platform observability: pipeline monitoring, cost tracking, performance dashboards, and capacity planning;
• Build self-service data infrastructure patterns that enable other teams to create and manage their own data pipelines with guardrails.
• Build and maintain feature stores for ML model training and serving: offline (batch) and online (real-time) feature computation and storage;
• Design data pipelines for ML workflows: training data preparation, validation sets, evaluation datasets, and model monitoring data;
• Implement data versioning and reproducibility for ML experiments using DVC, LakeFS, or Delta Lake time travel;
• Build feedback loop infrastructure: capturing AI model predictions, user interactions, and ground truth labels for continuous model improvement;
• Design and implement data infrastructure for AI model monitoring: input drift detection, output quality monitoring, and population stability metrics.

Desenvolvedor DotNet Sr

Senior Backend Developer – Protheus

Senior Cloud Engineer

Cyber Threat Hunter

Senior Developer

Senior Data Engineer