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About This Role
Monoprice runs a high\-SKU direct\-to\-consumer e\-commerce business on a proprietary platform with Microsoft 365 as our productivity backbone. We have AI workspace tools, Copilot, and Claude deployed across the team, with Claude desktop in active use among power users. The gap is not tooling. It is connecting those tools to the data and workflows that would make them genuinely useful for business teams.
This role sits at the center of our AI enablement program. The work is equal parts technical execution and human enablement. You will build data pipelines and automations that make our systems accessible to AI tools. You will train business teams to use what gets built. And you will document what you build so it compounds over time rather than creating a new dependency.
The forward\-looking technical work here is extending AI tooling into internal systems via Python connectors and data pipelines. Open\-platform automation experience is useful background. As the program matures, the work extends into AI\-native tooling: connecting business users to live system data through direct queries and natural language. But the foundation is reliable automation and accessible data first.
This role does not have a defined team under it. You may work alongside product management and change management resources, but you should expect to own the technical execution of the AI enablement program independently and to build the program's reach through training and documentation, not headcount.
What This Looks Like in Practice
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A department head tells you their team spends three hours every week pulling data from two systems into a spreadsheet, reformatting it, and distributing it to four people. Before you open any tool, you spend time understanding: what data are they actually pulling, where does it live in our source systems, what format do they need it in, and what happens after distribution. You come back with a clear picture of what is accessible, what the data pipeline looks like, and what the minimum viable solution is. You build it. You make sure the team can use it without you. You document it so the next person can extend it.
Some of this work is a Power Automate flow pulling from SharePoint. Some of it is connecting our SQL Server source systems to a Postgres destination that an AI tool can query. Some of it is configuring an MCP server so a business user can ask a natural language question against live business data. You size the problem and choose the right approach. You do not default to the most technically interesting solution.
What You Will Do
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Data Access and Pipeline Work
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- Build data pipelines that make source system data (SQL Server, M365\) accessible to AI tools and business users. The direction is source systems out to accessible destinations: Postgres, CSV, or direct AI tool integration.
- Build Python connectors and API integrations that extend AI tooling into internal data sources and systems. MCP server configuration is a growth area as the program scales, not a day\-one requirement.
- Understand the data structure of our source systems well enough to scope what is buildable before committing to a solution. SQL Server is the source. It is not interchangeable with downstream destinations.
- Evaluate and use data integration tooling (Airbyte or equivalent) where appropriate. Know when a Python script or direct connector is the simpler answer.
Workflow Automation
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- Build and deploy workflow automations using Microsoft Power Automate, Copilot Studio, and open\-platform tools where they fit the problem. Prefer the simplest tool that solves the problem reliably.
- Own the full lifecycle: discovery, build, deployment, adoption, documentation. An automation nobody uses or nobody can maintain is not a completed project.
- Maintain a prioritized automation and data pipeline backlog. Communicate progress and blockers to leadership and department heads.
Business Discovery and Requirements
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- Conduct workflow and data discovery sessions with non\-technical business teams. The job in these sessions is to understand the problem and the underlying data before proposing any solution.
- Scope requirements to the minimum viable solution. Not every use case needs to be automated. Not every edge case needs to be handled in version one.
- Know when to tell a business user that an existing AI tool or automation already solves their problem if connected to the right data. Building something new is not always the answer.
Training and Enablement
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- Train business teams on AI tools and automations as they are deployed. Adoption is part of delivery. If the team cannot use it without you, the project is not done.
- Document what gets built: what it connects to, what data it uses, how to maintain it, and how to extend it. The goal is compounding capability, not a new dependency.
- Establish intake processes so business teams can request and prioritize AI enablement work without routing everything through you individually.
Boundaries
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- Route to Engineering when work requires changes to our proprietary e\-commerce or back\-office platform. That boundary is real. Automations interact with platform systems only through available data exports and read\-only data access. Platform changes are out of scope for this role.
What You Bring
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Required
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- Demonstrated hands\-on experience delivering data pipeline and workflow automation solutions end\-to\-end in an enterprise environment, including deployment and adoption, not just build.
- Ability to sit with a non\-technical business team, understand their workflow and the data behind it, and scope what is buildable before proposing a solution.
- SQL proficiency sufficient to understand source system data structures and write queries to extract and transform data for downstream use.
- Experience connecting source databases (SQL Server or equivalent) to downstream destinations (Postgres, CSV, API endpoints) using integration tooling or custom connectors.
- Microsoft 365 automation experience: Power Automate, Copilot Studio, SharePoint, Teams, Outlook.
- Python or JavaScript for connectors, transformations, and API integrations.
- Track record of automations and data pipelines that business teams actually use and can maintain.
Strong Signal
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- Background in business process analysis, operational improvement, or process engineering that crossed into technical execution. This is the profile that succeeds here.
- Experience training non\-technical users on automation tools or workflows and driving real adoption. If your definition of done includes the team using it without you, this part of the role will come naturally.
- Experience working without a dedicated data engineering team, where you had to figure out data access independently.
- API connector development experience. MCP server configuration is a plus but not a prerequisite; the right candidate will grow into it as the program matures.
- Familiarity with the Claude API. Useful context for reasoning\-heavy use cases that go beyond standard workflow automation.
- Prior work in e\-commerce, retail, or a high\-SKU catalog environment.
Salary Context
This $120K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Monoprice, 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Monoprice AI Hiring
Monoprice has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brea, CA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (593 positions) offer remote work, while 3,349 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,103 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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