Software Architect

Quanta Infrastructure Solutions Group, LLC•Houston, TX

21h•Onsite

About The Position

QISG leverages Quanta’s comprehensive resources to deliver collaborative solutions for energy infrastructure needs. We bring together engineering, safety, quality, procurement, QA/QC, scheduling, environmental planning, permitting, and land management expertise to design and execute complex infrastructure projects with efficiency, consistency, and safety at the core. We are actively modernizing how software is built and delivered across the organization, using cloud, data, and AI-driven technologies to accelerate execution and improve how teams work at scale. As a Software Architect, you will define and lead the technical foundation for how software is designed, built, deployed, and scaled across QISG. You will serve as the senior technical authority on high-impact initiatives, ensuring solutions are robust, secure, maintainable, and aligned to business needs. You will: Lead high-stakes software and AI engineering engagements end-to-end, from discovery and architecture through delivery and rollout. Define reference architectures, engineering standards, and reusable frameworks that guide how software is built across the organization. Establish best practices for CI/CD, testing, deployment, observability, versioning, and production support. Design and build full-stack solutions spanning frontend, backend, APIs, data systems, and integrations. Create scalable, production-grade platforms that enable analytics, automation, and AI-powered workflows. Ensure software solutions are secure, governed, and aligned with enterprise standards (identity, access control, data classification, and risk management). Configure observability and monitoring for applications, including AI-enabled systems, to track performance, usage, and reliability. Mentor and elevate engineering teams through design reviews, pair programming, coaching, and technical leadership. Partner directly with executives and business leaders to translate strategy into scalable technical solutions. Continuously improve engineering maturity by introducing reusable assets, accelerators, and standardized patterns. You will focus on building and enabling enterprise-grade software capabilities such as: Establishing engineering standards and software development best practices across teams. Designing and implementing CI/CD pipelines for rapid and reliable deployments. Building reusable frameworks, platforms, and accelerators that improve delivery speed over time. Developing full-stack applications that streamline workflows and enable better decision-making. Implementing observability, logging, and monitoring systems for both traditional and AI-driven applications. Partnering with business and technical stakeholders to ensure solutions are aligned with real operational needs. Capturing repeatable patterns into shared libraries and internal platforms to scale impact across engagements.

Requirements

8+ years of experience in software engineering, solutions architecture, ML/AI engineering, or technical consulting
Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics, or related field (or equivalent experience)
Strong software engineering fundamentals including version control, testing, CI/CD, and production-grade system design
Strong proficiency in modern programming languages such as Python and/or JavaScript/TypeScript
Experience designing and delivering full-stack systems in production environments
Working knowledge of enterprise security principles including IAM, data classification, and system governance
Experience with modern AI and software engineering tools including LLMs, agents, RAG, prompt/context engineering, and AI-assisted development
Proven experience taking systems from prototype to production, including monitoring, evaluation, scaling, and reliability engineering
Demonstrated ability to lead complex, multi-stakeholder technical initiatives with measurable business impact
Strong executive communication skills with the ability to translate technical decisions into business outcomes

Nice To Haves

Experience in energy, utilities, EPC, infrastructure, or other field-based industries
Experience building scalable internal platforms, frameworks, or developer accelerators
Experience establishing engineering practices in fast-scaling or transformation environments
Exposure to Claude, Anthropic APIs, or AI-native development tooling
Experience in forward-deployed or customer-facing engineering roles

Responsibilities

Lead high-stakes software and AI engineering engagements end-to-end, from discovery and architecture through delivery and rollout
Define reference architectures, engineering standards, and reusable frameworks that guide how software is built across the organization
Establish best practices for CI/CD, testing, deployment, observability, versioning, and production support
Design and build full-stack solutions spanning frontend, backend, APIs, data systems, and integrations
Create scalable, production-grade platforms that enable analytics, automation, and AI-powered workflows
Ensure software solutions are secure, governed, and aligned with enterprise standards (identity, access control, data classification, and risk management)
Configure observability and monitoring for applications, including AI-enabled systems, to track performance, usage, and reliability
Mentor and elevate engineering teams through design reviews, pair programming, coaching, and technical leadership
Partner directly with executives and business leaders to translate strategy into scalable technical solutions
Continuously improve engineering maturity by introducing reusable assets, accelerators, and standardized patterns
Establishing engineering standards and software development best practices across teams
Designing and implementing CI/CD pipelines for rapid and reliable deployments
Building reusable frameworks, platforms, and accelerators that improve delivery speed over time
Developing full-stack applications that streamline workflows and enable better decision-making
Implementing observability, logging, and monitoring systems for both traditional and AI-driven applications
Partnering with business and technical stakeholders to ensure solutions are aligned with real operational needs
Capturing repeatable patterns into shared libraries and internal platforms to scale impact across engagements