Senior Software Development Engineer

How You’ll Help Us Keep Climbing (Overview & Key Responsibilities) We are seeking a highly skilled and forward-thinking Senior Platform Engineer to design, build, and support enterprise platform capabilities that enable secure, scalable, and efficient software delivery across the organization.

This role focuses on improving developer productivity, engineering excellence, and operational efficiency through automation, DevOps practices, cloud technologies, and self-service platform solutions. The successful candidate will build and enhance CI/CD platforms, developer tooling, internal applications, automation frameworks, AI-enabled engineering capabilities, and Model Context Protocol (MCP) integrations that accelerate software delivery and improve the developer experience.

The ideal candidate brings strong expertise in DevOps, Platform Engineering, Infrastructure as Code (IaC), Kubernetes, automation, and software delivery practices, along with working knowledge of AWS cloud services. Working closely with development, infrastructure, architecture, and security teams, this individual will help establish reliable, scalable, and secure platform services while driving adoption of modern engineering and AI-assisted development practices across the organization.

Key Responsibilities

• Design, build, and support enterprise platform capabilities that improve software delivery, developer productivity, and operational efficiency.
• Own and enhance the organization’s DevOps ecosystem, including GitLab, GitLab Runners, Nexus Repository, CI/CD pipelines, and associated platform services.
• Develop and maintain automation solutions, Infrastructure as Code (IaC), reusable CI/CD frameworks, and self-service capabilities that simplify and accelerate software delivery.
• Support and optimize cloud-based infrastructure and services, ensuring reliability, scalability, security, performance, and cost efficiency.
• Drive adoption of modern Platform Engineering and DevOps practices by enabling standardized development workflows, reusable solutions, developer portals, and Internal Developer Platform (IDP) capabilities.
• Establish and improve engineering effectiveness through DORA metrics, platform analytics, operational insights, and continuous improvement initiatives.
• Design, build, and maintain Model Context Protocol (MCP) servers and AI integrations that securely connect AI assistants, developer tools, enterprise systems, APIs, and knowledge platforms.
• Leverage AI-assisted engineering tools and emerging AI technologies to improve software development, automation, troubleshooting, documentation, and platform operations.

What You Need To Succeed (Minimum Qualifications)

• Bachelors Degree in Computer Science, Information Systems or related technical field is preferred.
• 4-7 years of experience in DevOps Engineering, Platform Engineering, Cloud Engineering, or related technical roles.
• Strong hands-on experience with GitLab, GitHub, or similar DevOps platforms, including source code management, CI/CD pipeline administration, build runner/agent management, repository governance, and platform integrations.
• Strong experience designing and supporting enterprise-scale CI/CD platforms and software delivery pipelines.
• Hands-on experience with AWS cloud services and cloud-native platform architectures.
• Experience with Infrastructure as Code technologies such as AWS CloudFormation, or AWS CDK.
• Experience with containerization and orchestration technologies including Docker, Kubernetes
• Proficiency in Python, Shell scripting, Node.js, or similar programming languages.
• Experience building automation frameworks, internal tooling, APIs, integrations, or platform services.
• Experience implementing monitoring, logging, observability, and operational reporting solutions.
• Experience defining and leveraging DORA metrics and engineering productivity measurements.
• Demonstrated experience using AI-assisted development tools (e.g., Copilot, Claude, Kiro) for code generation, automation, documentation, testing, troubleshooting, and engineering productivity improvements while validating and owning AI-generated outputs.
• Experience building and integrating AI-powered applications, agents, MCP servers, APIs, or automation solutions that connect AI models with enterprise systems, developer tools, and knowledge sources.
• Strong understanding of software development lifecycle (SDLC), DevOps practices, CI/CD pr…