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Moreton Capital Partners

Systematic commodities investment manager using AI

DevOps Engineer

Cloud Engineering +1Confirmed live in the last 24 hours
Full-timeMexico City, CDMX, MexicoNo salary listed

About the Job

Requirements

  • Strong knowledge of AWS.
  • Strong Linux skills.
  • Experience with Terraform and Infrastructure as Code.
  • Strong Python scripting skills.
  • Experience with Docker.
  • Understanding of CI/CD and GitHub Actions.
  • Good networking fundamentals.
  • Familiarity with PostgreSQL and Redis.
  • Strong troubleshooting and problem-solving skills.

Responsibilities

  • Build and maintain infrastructure across AWS, including ECS, EC2, S3, VPC, CloudFront, WAF, and ALB.
  • Manage infrastructure using Terraform and Infrastructure as Code.
  • Support Docker-based production and development environments.
  • Help improve infrastructure reliability, security, and performance.
  • Build and maintain CI/CD workflows using GitHub Actions.
  • Use Python and shell scripting to automate operational and infrastructure tasks.
  • Support deployment processes and troubleshoot application and infrastructure issues.
  • Help improve engineering workflows and reduce manual processes.
  • Monitor applications and infrastructure using Datadog.
  • Support Aurora/PostgreSQL, Redis, and Airflow environments.
  • Help improve logging, monitoring, alerting, and production visibility.
  • Investigate infrastructure and application issues across Linux, networking, databases, and AWS.

Desired Qualifications

  • AWS certification.
  • Experience with ECS, EC2, CloudFront, WAF, and ALB.
  • Familiarity with Datadog and Apache Airflow.
  • Experience using AI coding tools such as Claude Code, Codex, or similar tools in an engineering workflow.
  • Claude / Anthropic training or certification.
  • Interest in financial markets, trading systems, or fintech.

About the Company

Moreton Capital Partners

Systematic commodities investment manager using AI

Moreton Capital Partners runs market-neutral commodity funds using a technology-driven approach that blends agentic AI, large language models, machine learning, and quantitative research to find relative-value opportunities across energy, metals, and agriculture. It processes hundreds of thousands of data points per day—covering supply and demand, market positioning, and volatility—to generate trading signals that guide fund allocations and risk controls in over 100 commodities. The firm differentiates itself with an adaptive, data-rich method that evolves with changing market structure and a team with deep experience in both physical trading and systematic investing, focusing on institutional clients seeking stable returns rather than simple price bets. Its goal is to deliver attractive risk-adjusted returns for institutions by exploiting inefficiencies and weather-related or inflation risks while keeping a market-neutral posture.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Delaware, United States
Founded
Not listed

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