Senior Engineer – AI Systems & Automation - Remote

Overview

The Senior Engineer – AI Systems & Automation is responsible for designing, building, and deploying full-stack applications and microservices that form the foundation of AI-powered security and compliance agents.

In Short

• Design, code, and maintain APIs, services, and user-facing components.
• Implement and maintain CI/CD pipelines, deployment processes, and automation tools.
• Build and optimize microservices running on AWS and Kubernetes.
• Collaborate with architects, product managers, and AI engineers to translate requirements into technical solutions.
• Ensure system reliability, performance, and security through robust engineering practices.
• Troubleshoot, debug, and resolve complex technical issues in production systems.
• Contribute to technical documentation, coding standards, and best practices.
• Participate in code reviews and mentor junior developers when needed.

Requirements

• 7–10+ years in software development with proven experience in full-stack roles.
• Proficiency in Python and experience with JavaScript/TypeScript frameworks.
• Strong background in API development and system integration.
• Hands-on experience with AWS cloud services and Kubernetes orchestration.
• Solid understanding of DevOps practices, CI/CD pipelines, and infrastructure automation.
• Exposure to microservice architectures and distributed systems.
• AWS Developer or Architect certifications, DevOps certification (preferred).
• Strong coding discipline and attention to detail.
• Collaborative and team-oriented mindset with excellent communication skills.
• Problem-solver with the ability to learn quickly and adapt to new technologies.
• Organized and capable of managing multiple priorities in fast-paced environments.
• Passion for automation, scalability, and building robust systems.
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.

Benefits

• Previous experience in automation engineering, big data, or analytics environments.
• Additional programming languages (e.g., Go, Java, Rust).
• Familiarity with data engineering practices and tools.
• Hands-on experience with observability, logging, and monitoring frameworks.