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About This Role
Embedded AI Engineer – IT
Company: Gigapower LLC
Dept./Org.: Trans./Strategy
Location: Virtual (Remote)
Position Type: PW
Who We Are
Gigapower is building next\-generation fiber broadband infrastructure that expands high\-speed connectivity to communities across the United States. We are committed to innovation, operational excellence, and leveraging technology to deliver smarter, faster, and more scalable solutions. As an AI\-forward organization, we encourage employees at every level to embrace emerging technologies, continuously expand their AI capabilities, and actively incorporate AI tools into their daily work to improve productivity, decision\-making, collaboration, and business outcomes.
Position Summary
The Embedded AI Engineer serves as the dedicated AI partner for Gigapower's IT organization. This role is responsible for identifying, developing, and deploying AI\-powered solutions that improve network monitoring, network operations, maintenance, performance optimization, capacity management, and operational efficiency across Gigapower's fiber network environment. Working directly with IT, Network Operations, and technology stakeholders, the Embedded AI Engineer will identify high\-value opportunities for automation, operational intelligence, and workflow optimization while accelerating AI adoption throughout the organization.
Key Responsibilities
- Partner with IT and Network Operations teams to understand monitoring, maintenance, incident management, and operational workflows.
- Identify and prioritize opportunities where AI can improve network visibility, operational efficiency, performance, and reliability.
- Build and deploy AI\-powered automations, RAG applications, agents, copilots, and internal productivity tools.
- Develop solutions that support network monitoring, alert management, root\-cause analysis, capacity planning, and load balancing activities.
- Create intelligent tools that help teams identify trends, predict issues, and accelerate issue resolution.
- Collaborate with Data Engineering and AI teams on enterprise\-scale platforms and shared data initiatives.
- Coach stakeholders on effective use of Microsoft Copilot, Claude, ChatGPT, and emerging AI technologies.
- Measure adoption, quality, and business outcomes and continuously improve delivered solutions.
- Reduce manual effort through workflow automation, reporting enhancements, and operational intelligence capabilities.
- Identify scalable AI use cases that can be leveraged across additional technology and operational functions.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline, or equivalent practical experience.
- 1 to 3 years of experience developing software, analytics, automation, data, or AI\-based solutions.
- Hands\-on experience with LLMs, prompt engineering, RAG, AI agents, or automation workflows.
- Strong proficiency in Python.
- Working knowledge of SQL and experience working with structured datasets.
- Ability to take solutions from concept through deployment and user adoption.
- Strong communication skills and the ability to build relationships with technical and non\-technical stakeholders.
- Self\-starter mindset with comfort operating in fast\-paced and ambiguous environments.
- Strong analytical, problem\-solving, and critical\-thinking skills.
- Enthusiasm for learning technology operations, network infrastructure, and business processes.
Preferred Qualifications
- Experience in network operations, network monitoring, IT operations, telecommunications, or NOC environments.
- Experience supporting broadband, fiber, telecommunications, infrastructure, or large\-scale technology environments.
- Experience with Azure, Snowflake, cloud platforms, monitoring tools, or operational analytics environments.
- Experience with change management, training, enablement, or technology adoption initiatives.
Key Competencies
- Network Operations Optimization: Improves operational efficiency, reliability, and visibility through technology and automation.
- AI Solution Development: Designs and delivers practical AI\-powered solutions that create measurable business value.
- Technical Partnership: Builds trusted relationships with IT and operational stakeholders to solve complex challenges.
- Data\-Driven Decision Making: Uses data, analytics, and AI to support operational decisions and performance improvements.
- Automation \& Innovation: Identifies opportunities to eliminate manual effort and improve productivity.
- Communication \& Influence: Clearly explains technical concepts and drives successful adoption of new capabilities.
- Learning Agility: Quickly develops expertise in new technologies, systems, and operational domains.
About Us
Gigapower is building the next generation of broadband infrastructure. Through innovative fiber network deployment and strategic partnerships, we are helping expand high\-speed connectivity to communities and businesses across the United States. Our mission is to deliver reliable, scalable, and future\-ready broadband solutions that empower economic growth, education, healthcare, and digital innovation.
At Gigapower, we value collaboration, accountability, integrity, and continuous improvement. We are committed to creating an environment where employees can contribute meaningful work, develop professionally, and make a lasting impact on the communities we serve. As we continue to grow, we seek talented individuals who are passionate about excellence and eager to help shape the future of connectivity.
EEO
Gigapower is an Equal Opportunity Employer and is committed to creating a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, national origin, ancestry, citizenship status, age, disability, medical condition, genetic information, sex, pregnancy, sexual orientation, gender identity or expression, marital status, veteran status, military status, or any other characteristic protected by applicable federal, state, or local law.
Gigapower is committed to providing reasonable accommodation to qualified individuals with disabilities and to applicants throughout the recruitment process. If you require assistance or accommodation, please notify a member of our Talent Acquisition team.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gigapower, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Gigapower AI Hiring
Gigapower has 7 open AI roles right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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