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About This Role
POSITION SUMMARY:
The Associate AI Engineer is an early\-career builder responsible for moving approved ideas from a governed backlog into reliable, well\-documented solutions. Working as part of Pogue's AI production team, this individual develops, tests and supports AI and automation capabilities built on Pogue's Microsoft Fabric data platform, and contributes to the enterprise ontology and knowledge foundation that those solutions depend on.
This is a hands\-on development role with structured mentorship. Design review and pairing with Pogue's Principal Solutions Architect are a standing part of the work, and this individual is expected to partner directly with business owners so that what gets built is actually used.
EXPERIENCE \& EDUCATION:
0–3 years' professional experience in software development, data engineering or a related technical role.
Bachelor's degree in computer science, software engineering, information systems, data science or a related field, or commensurate experience.
PRIMARY RESPONSIBILITIES:
AI Solution Development:
- Develop, test \& improve bounded AI and automation capabilities using VS Code, SQL, APIs, Git \& Pogue\-approved cloud services
- Support solutions such as permission\-aware enterprise search, retrieval\-augmented generation (RAG), internal assistants, workflow support \& AI\-enabled analytics
- Translate approved backlog items into requirements, technical tasks, acceptance criteria \& small releases that can be measured \& safely supported
- Create prototypes when discovery is needed, then help convert validated prototypes into maintainable services \& reusable patterns
- Participate in design review with the Principal Solutions Architect before development begins \& carry approved designs through to release
Data \& Enterprise Knowledge:
- Work with Microsoft Fabric lakehouse data, governed Gold business objects, semantic models, metadata \& shared business definitions
- Help connect structured data \& approved documents through secure APIs \& retrieval patterns while preserving source permissions, citations, versions \& effective dates
- Contribute to Pogue's enterprise ontology \& knowledge foundation by documenting objects, relationships, definitions, ownership \& authoritative sources
- Build only on data confirmed as validated in the Gold layer
Quality, Security \& Trust:
- Document architecture, assumptions, decisions, data flows, interfaces, tests, deployment steps \& operating runbooks so another team member can understand \& support the solution
- Evaluate accuracy, failure modes, access behavior, latency, cost \& usefulness; turn results into clear release recommendations
- Follow Pogue's identity, role\-based access, data\-classification, approved\-model, logging, monitoring \& human\-approval requirements
- Surface uncertainty, security concerns \& data\-quality issues early; propose practical options instead of hiding risk
Business Partnership \& Adoption:
- Work directly with business owners \& subject\-matter experts to understand the decision or workflow before selecting a technical approach
- Explain tradeoffs in plain language, ask focused questions, demonstrate progress \& incorporate feedback from technical \& nontechnical teammates
- Support adoption with concise guides, examples, training materials \& responsive follow\-through after launch
- Support the AI Champions Network as solutions roll out to project teams
REQUIRED SKILLS
- Team Player
- Teachable
- Curious \& self\-directed
- Able to write \& troubleshoot code in VS Code
- Working knowledge of SQL
- Working understanding of REST APIs, Git\-based collaboration, testing \& basic cloud concepts
- Secure handling of credentials \& data
- Able to explain a technical project, the decisions personally made, the tradeoffs \& how the result was validated
- Clear written communication
- Able to relate to \& communicate with a diverse group of professionals
- Ability to work individually \& as part of a team
- Self\-motivated \& driven
- Highly organized \& detail oriented
- Highly analytical thinker
- Positive Attitude
- Internal \& external customer service
- Willingness to ask for context when requirements are incomplete
- Minimum 20 hrs of Continued Education (yearly)
TECHNICAL PROGRAM EXPERIENCE (not required):
- Microsoft Azure or Fabric, OneLake or lakehouse patterns, Power BI, Azure AI Search, Azure OpenAI or Azure AI Foundry
- LLM applications, RAG, embeddings, evaluation, prompt or model lifecycle management, agents or workflow automation
- Coursework or project work in knowledge representation, semantic modeling, enterprise ontologies or knowledge graphs — including Protégé, OWL, RDF or SPARQL
- Metadata, data lineage, document management or construction \& project\-control systems
- TypeScript, C\# or another modern language
- CI/CD, containerized services, monitoring, cost management or secure enterprise integration
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 Pogue Construction, 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. Entry-level AI roles across all categories have a median of $110,000.
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.
Pogue Construction AI Hiring
Pogue Construction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McKinney, 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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