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Senior Associate Platform Engineer

Location: Plano, TX – Hybrid, 1–2 days per week in office

Required Certification

Candidates must hold at least one of the following certifications. A copy of the certification must be provided with the submission:

• AWS Certified Cloud Practitioner
• Microsoft Azure Fundamentals
• Google Cloud Digital Leader

Position Summary

The Senior Associate Platform Engineer supports the development, configuration, and maintenance of enterprise platform services, automation solutions, and cloud-based infrastructure. This role builds reusable tools, frameworks, platform capabilities, and self-service solutions that improve engineering productivity, software delivery, reliability, and operational efficiency.

The role has a strong focus on SDLC automation, cloud engineering, DevOps, and AI-enabled development. The Senior Associate Platform Engineer works with engineering and platform teams to implement CI/CD pipelines, infrastructure automation, observability, developer tools, and AI-powered solutions across the AI-Driven Development Lifecycle (AIDLC).

The ideal candidate is a hands-on engineer who enjoys solving technical problems, automating manual processes, building reusable solutions, and applying emerging technologies to improve software engineering workflows.

Key Responsibilities

Platform Engineering & Developer Experience

• Build and maintain scalable platform services, shared services, and engineering capabilities.
• Develop reusable APIs, SDKs, frameworks, templates, and developer tools.
• Build self-service capabilities that simplify development and reduce engineering dependencies.
• Improve developer onboarding, development, testing, deployment, and support workflows.
• Establish and follow platform engineering standards, patterns, and secure delivery practices.

Automation & AI-Enabled Engineering

• Design and implement automation solutions across the software development lifecycle.
• Automate development, testing, deployment, operations, governance, and security processes.
• Build reusable automation frameworks, workflows, and engineering tools.
• Leverage AI-assisted development tools to improve engineering productivity and reduce manual effort.
• Support the implementation of AI agents and intelligent workflows across engineering processes.

AI Engineering & Agent Platforms

• Build AI-powered engineering capabilities and developer experiences.
• Develop and integrate AI agents, workflow automation, and agent orchestration solutions.
• Work with LLMs, prompt engineering, context engineering, RAG, and MCP integrations.
• Integrate AI tools and platforms such as GitHub Copilot, AWS Kiro, and Amazon Bedrock.
• Evaluate emerging AI technologies and identify opportunities to improve engineering efficiency.

Cloud & DevOps

• Develop and support cloud-native solutions, primarily within AWS environments.
• Implement and maintain CI/CD pipelines and automated deployment processes.
• Support Infrastructure as Code (IaC), cloud automation, and modern DevOps practices.
• Build reliable, scalable, and secure platform capabilities.
• Implement automated controls, deployment guardrails, and policy enforcement.

Observability & Reliability

• Implement monitoring, logging, observability, and operational intelligence capabilities.
• Troubleshoot platform and application issues and perform root-cause analysis.
• Support reliability, resilience, scalability, and performance improvements.
• Leverage automation and AI-assisted tools to identify and resolve operational issues.
• Use operational metrics and insights to continuously improve platform performance.

Security & Governance

• Incorporate security and governance controls into platform and engineering solutions.
• Support secure-by-design practices throughout the software development lifecycle.
• Implement automated compliance validation and policy enforcement.
• Partner with security and architecture teams to ensure alignment with enterprise standards.
• Help develop guardrails that balance engineering agility with security and compliance requirements.

Minimum Qualifications

• Bachelor’s degree in Information Technology, Computer Science, Computer Information Systems, Software Engineering, Mathematics, Statistics, or a related field; equivalent relevant experience may be considered.
• 2–5 years of hands-on software or platform engineering experience.
• Experience programming in one or more modern programming languages.
• Experience building automation solutions, engineering platforms, developer tools, reusable components, shared services, or self-service capabilities.
• Experience with cloud technologies, preferably AWS.
• Understanding of DevOps, CI/CD, cloud-native development, and software delivery practices.
• Strong problem-solving and troubleshooting skills.
• Ability to collaborate effectively with engineering, product, architecture, and platform teams.
• At least one required cloud certification: AWS Cloud Practitioner, Microsoft Azure Fundamentals, or Google Cloud Digital Leader.

Preferred Qualifications

• Experience with AI-assisted software development, AI agents, or intelligent automation.
• Experience with GitHub Copilot, AWS Kiro, Amazon Bedrock, RAG, MCP, or LLM-based applications.
• Experience with Infrastructure as Code and cloud automation.
• Experience with observability, monitoring, debugging, and reliability engineering.
• Additional certifications in cloud, DevOps, platform engineering, software development, AI/ML, or related technologies.

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