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About This Role
At IEM, we're not just building innovative electrical distribution systems, we're shaping the future. IEM is dedicated to delivering world\-class solutions for complex power needs. After 75 years, we continue to push the boundaries of what's possible. Whether you're an experienced professional or just starting out, you'll have the opportunity to contribute, grow, and make a lasting impact on industries that power the world's most dynamic markets.
Location: Austin, TX \- USA
Reports To: VP, Enterprise AI \& Automation
Salary Range: $106,000 \- 132,000
Position Summary
IEM is seeking a driven AI Operations Analyst to serve as the front door for enterprise AI at IEM. You will own the intake of new AI project ideas, help guide proof\-of\-concept efforts, and support employees across the company in successfully adopting AI tools. You will also manage the operational side of AI enablement, including tool access requests, license administration, and user onboarding/offboarding. This is a high\-visibility role for someone who wants to help shape how a growing enterprise adopts and scales practical AI use, with requests, licenses, and support handled efficiently and in compliance with our governance standards.
Key Responsibilities:
- AI Project Intake: Serve as the front door for new AI project and use\-case ideas across IEM, gathering requirements, documenting business needs, and routing qualified requests to the appropriate team members for scoping and prioritization.
- User Onboarding \& Offboarding: Provision new users on AI platforms, walk them through initial setup, and provide the guidance and resources they need to become productive quickly; deprovision access and reclaim licenses for departing or transitioning employees.
- Tool Request Intake \& Triage: Manage the intake queue for new AI tool access requests, verify business justification, and coordinate approvals with managers and IT/security stakeholders.
- License Administration: Assign, track, and reconcile AI tool licenses and seats across platforms, monitor utilization, and flag underused or over\-allocated licenses for optimization.
- POC Support: Assist in the build\-out of proof\-of\-concept solutions by preparing sample data, configuring tools, coordinating stakeholder feedback sessions, and tracking POC status and outcomes.
- Platform Administration: Support day\-to\-day administration of AI platforms, including access controls, security group management, and routine configuration changes.
- AI Literacy \& Training: Help plan and deliver AI literacy programming across IEM, including onboarding walkthroughs, workshops, and training materials that build organization\-wide comfort and proficiency with approved AI tools\-one of the most direct ways this role drives AI adoption company\-wide.
- User Support \& Enablement: Act as the first point of contact for employee questions about AI tools, troubleshooting common issues and escalating complex technical problems to senior team members.
- Usage Monitoring \& Reporting: Track platform adoption metrics and generate regular reports on active users, request volumes, and license utilization to inform planning decisions.
- Operational Documentation: Maintain up\-to\-date standard operating procedures (SOPs), FAQs, and knowledge base articles covering intake processes, tool usage, and common troubleshooting steps.
- Compliance Checks: Perform routine audits to confirm AI tool usage and access align with IEM's AI governance policies and data security standards.
- Qualifications Required:
- Experience: 1\-3 years in IT operations, help desk/technical support, business analysis, or a related coordination role.
- Hands\-On AI Tool Experience: Demonstrated experience using generative AI tools to automate workflows, improve productivity, or solve business problems.
- Organization \& Follow\-Through: Demonstrated ability to manage multiple requests or tickets simultaneously, track them to completion, and keep stakeholders informed of status.
- Communication: Strong written and verbal communication skills, with the ability to explain basic technical concepts to non\-technical users.
Preferred Skills \& Experience:
- Business analysis fundamentals: experience gathering requirements or documenting a process or workflow, especially with an eye toward where AI could remove manual steps.
- Automation curiosity: comfort learning basic scripting or low\-code automation to streamline routine tasks and connect AI tools to other business systems.
- Reporting: basic experience building simple reports or trackers in Excel, Power BI, or similar tools.
- Relevant coursework or certifications in artificial intelligence, project management (PMP), or change management are a plus.
General Competencies:
- Customer Service Orientation: Patient, approachable, and responsive when supporting users of varying technical ability.
- Problem\-Solving Mindset: Methodical approach to triaging issues and knowing when to escalate.
- Adaptability: Eagerness to learn new platforms and work across multiple AI tools and use cases.
- Detail Orientation: Careful, accurate documentation and tracking of requests, licenses, and tickets.
- Active Listening: Skilled at asking clarifying questions to fully understand a requester's needs.
- Business Impact Orientation: Focuses on delivering measurable improvements in productivity, quality, cost, or employee experience rather than administering tools for their own sake.
- Growth Mindset: Genuine interest in building a long\-term career in enterprise AI operations, with a path toward more advanced analyst or engineering responsibilities.
Job Location:
- Position is located in Austin, TX.
- Position is primarily remote with some travel required initially to IEM facilities or for project kickoffs.
- Candidate may be required to work in\-office in the future as business needs evolve.
Compensation \& Benefits:
- Competitive salary and benefits package commensurate with experience.
- Opportunity to build hands\-on experience with cutting\-edge enterprise AI tools and platforms.
- Direct mentorship from senior AI and Salesforce/MuleSoft technical staff.
- Collaborative and supportive work environment with a clear growth path into more advanced AI roles.
- Professional development and training resources.
Why Join IEM
At IEM, you'll join a team that powers some of the world's most ambitious projects. We're engineers, makers, and problem\-solvers who thrive on tackling complex challenges and delivering solutions that keep industries moving forward. If you're driven, collaborative, and ready to make an impact, we'd love to hear from you. Your creativity and passion can help us achieve great things—come be part of the journey.
Learn more about IEM at https://www.iemfg.com
We offer comprehensive and competitive benefits package designed to support our employees' well\-being, growth, and long\-term success. View a snapshot of our benefits at https://www.iemfg.com/careers
Recruiting Scams
Beware of recruiting scams. IEM never charges candidates fees, and all recruiter emails come from an @iemfg.com address. If you suspect fraudulent activity, do not share personal information and report it to us at iemfg.com/contact
Non\-Discrimination Statement
IEM does not discriminate against any applicant based on any characteristic protected by law.
Privacy
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Salary Context
This $106K-$132K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Industrial Electric Manufacturing, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($119K) sits 46% below the category median. Disclosed range: $106K to $132K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Industrial Electric Manufacturing AI Hiring
Industrial Electric Manufacturing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $132K - $132K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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