Embedded AI Engineer

Gigapower is building AI capabilities to support its engineering teams across the full engineering workflow, from early planning through documentation and operations. As the dedicated Embedded AI partner, you will identify high-impact use cases, develop AI-powered automations and copilots, and help teams adopt tools that measurably improve productivity, quality, and permitting efficiency. The role is based in Dallas, TX with remote flexibility.

What you’ll do

• Partner with Engineering, Design, and Permitting teams to understand workflows, challenges, and opportunities for AI-driven improvement.
• Identify, prioritize, and deliver AI solutions that improve engineering productivity, design quality, permitting efficiency, and project execution.
• Build and deploy AI-powered automations, retrieval-augmented generation (RAG) applications, intelligent agents, copilots, and internal engineering tools.
• Develop solutions to streamline permit package creation, engineering documentation, design reviews, and standards compliance.
• Create AI-enabled capabilities that support HLD and LLD development, engineering analysis, and network planning activities.
• Collaborate with Data Engineering and AI teams on enterprise-scale solutions using shared platforms, analytics, and operational data.
• Coach engineering stakeholders on using AI tools including Microsoft Copilot, Claude, and ChatGPT.
• Measure solution adoption, quality, and business impact, then iterate based on stakeholder feedback.
• Work with engineering leadership to identify scalable AI opportunities across network design and deployment activities.
• Use AI-powered tools to improve personal productivity and accelerate solution development.

Requirements

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, or a related discipline (or equivalent practical experience).
• Experience: 1 to 3 years developing software, analytics, automation, or AI-based solutions.
• Hands-on experience building applications using Large Language Models (LLMs), including prompting, RAG, AI agents, automation workflows, or tool integrations.
• Python proficiency.
• Experience taking solutions from concept through deployment and user adoption.
• Working knowledge of SQL and experience working with structured datasets.
• Strong communication skills, including the ability to explain technical concepts to non-technical audiences.
• Ability to operate independently and manage competing priorities in a fast-paced environment.
• Strong problem-solving, analytical, and critical-thinking skills.
• Enthusiasm for learning new technologies and understanding complex business operations.

Technologies you may use

• Python, SQL
• Large Language Models (LLMs), retrieval-augmented generation (RAG), AI agents
• Microsoft Copilot, Claude, ChatGPT
• Azure, Snowflake, cloud-based analytics platforms
• Engineering systems

Preferred qualifications

• Experience in fiber engineering, telecommunications engineering, network design, permitting, outside plant (OSP), or broadband infrastructure.
• Familiarity with HLD, LLD, fiber routing, network planning, or engineering documentation processes.
• Working knowledge of SQL plus experience with structured, geospatial, or GIS-based datasets.
• Experience with Azure, Snowflake, cloud-based analytics platforms, or engineering systems.
• Experience supporting change management, technology adoption, enablement, or training initiatives.

Key competencies

• Engineering Process Optimization: Identifies opportunities to improve engineering efficiency, quality, and consistency through technology.
• AI Solution Development: Designs and delivers AI-powered solutions that create measurable business value.
• Technical Partnership: Builds trusted relationships to solve complex operational challenges.
• Problem Solving & Analysis: Applies data-driven thinking to engineering and network design challenges.
• Innovation & Automation: Finds ways to eliminate manual work and improve productivity.
• Communication & Influence: Explains technical concepts clearly, gains stakeholder buy-in, and drives adoption.
• Learning Agility: Quickly learns new technologies, engineering processes, and business domains to maximize impact.