*** Arganteal accepts applications from direct candidates only.
We do not work with third-party recruiters or staffing agencies.
*** Required Country Location: Costa Rica, Peru, Argentina, Brazil, Columbia, South Africa, Mexico, or Panama.
*** This is full time work at 40 hours per week Overview:Our client seeks a motivated Senior DevOps Engineer, Data & AI to join their team in building a groundbreaking, modular platform from the ground up.
This platform digitizes and contextualizes multi-modal sensor data from both digital and physical environments into specialized time-series, graph, and vector databases—powering real-time analytics, compliance, and AI-driven context mapping.This role is adecuado for a DevOps leader with strong expertise in data engineering, distributed systems, and applied AI, who thrives on automation, scalability, and production-grade deployments across hybrid and cloud environments.
Key Responsibilities:Platform Automation & InfrastructureArchitect, automate, and manage infrastructure for data ingestion, contextualization, and visualization modules (Data, Access, & Agents).
Build CI/CD pipelines for sensor collection agents across heterogeneous systems (Windows, Linux, macOS, mobile, IoT).
Implement and automate real-time ingestion pipelines using Apache Kafka, Apache NiFi, Redis Streams, or AWS Kinesis.Database & Data Layer EngineeringDeploy, scale, and optimize multi-modal databases:Time-series: MongoDB, InfluxDB, TimescaleDB, or AWS TimestreamGraph: Neo4j (Cypher, APOC, graph schema design)Vector: Qdrant, FAISS, Pinecone, or WeaviateAutomate deployment and monitoring of a Database Access Layer (DBAL)



to unify queries across multiple database engines.Experiment with or extend Model Context Protocol (MCP) or similar standards for cross-database and multi-agent interoperability.Data Streaming & IntegrationEngineer low-latency pipelines for event streams (syslog, telemetry, keystrokes, IoT feeds, cloud service logs).
Collaborate with frontend engineers to integrate visual mapping UIs with scalable back-end pipelines.Optimization, Reliability & ScalabilityOptimize system and database performance using down-sampling, partitioning, and caching techniques.Design solutions for horizontal scaling and containerized deployment (Docker, Kubernetes, OpenShift).
Apply infrastructure-as-code practices to achieve resilience, reproducibility, and rapid iteration under real-world constraints.Collaboration & LeadershipPartner with compliance, security, and business stakeholders to ensure systems meet regulatory and operational requirements.Conduct architecture reviews, lead DevOps best practices, and mentor junior engineers on automation, scalability, and observability.
Required Skills & Experience:Programming: Strong proficiency in Python and Node.js (C++ a plus).
Streaming: Proven hands-on experience with Kafka, NiFi, Redis Streams,



or AWS Kinesis.Databases:Time-series: MongoDB, InfluxDB, TimescaleDB, or AWS TimestreamGraph: Neo4j (Cypher, APOC)Vector: Qdrant, FAISS, Pinecone, or WeaviateAI & Agents: Experience with—or strong interest in—Agentic AI frameworks, multi-agent orchestration, and context-aware data processing.Data Interchange: Familiarity with MCP-like protocols or standardized APIs for multi-database access.Cloud & Infrastructure: Hands-on with AWS, Azure, or GCP, plus containerization and orchestration (Docker, Kubernetes, OpenShift).
DevOps Expertise: Deep understanding of CI/CD pipelines, IaC (Terraform/Ansible), monitoring/observability, distributed systems, and microservices security.Problem Solving: Strong debugging skills, automation mindset, and ability to balance speed, scalability, and compliance in production systems.
Preferred Skills:Machine Learning/NLP integration into multi-modal pipelines.CI/CD automation and DevOps practices.Knowledge of enterprise integration patterns, event-driven systems, and zero-trust security models.Experience with compliance frameworks (NERC CIP, FedRAMP, GDPR, SOX).
Qualifications:Bachelor's degree in Computer Science, Engineering, or related field (or equivalent hands-on experience).5+ years professional software development with data-intensive or AI-driven systems.Proven experience designing, deploying, and scaling modular platforms in production.
*** Arganteal accepts applications from direct candidates only.
We do not work with third-party recruiters or staffing agencies.

📌 Senior Devops Engineer (Nuevo León)
🏢 Arganteal
📍 Nuevo León