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About This Role
Company Overview
MGI Inc. contractor working primarily on federal healthcare construction projects. We are a Service\-Disabled Veteran\-Owned Small Business and a repeat Inc. 5000 honoree. We are investing heavily in AI automation so our staff can focus on judgment, relationships, and skilled work rather than repetitive administrative tasks.
Job Summary
MGI Inc. is hiring an AI Engineer to help build and expand our internal AI automation platform. This is not an exploratory or research position. We have a working production system in daily use across our project management, accounting, contracting, and IT departments, and we are looking for an experienced engineer to help extend it, harden it, and bring new departments online. You will design, build, and maintain AI agents that perform real work: answering employee questions from live company data, generating documents, monitoring systems, analyzing bids, and automating recurring workflows. Some agents serve general employee needs, and others are purpose\-built for a single specialized task. You will create and maintain systems end to end, from the API integration through to the deployed agent that employees rely on every day.
WHAT YOU WILL WORK WITH
Our platform is substantial and already in production:\- A multi\-agent system with several named AI agents, each with its own role, tool set, and company identity, serving different departments\- Roughly 200,000 lines of Python powering agent tools and integrations\- Chat\-based agents that employees message directly in Microsoft Teams and receive substantive answers from\- A retrieval system holding millions of indexed documents, including company email and attachments, construction codes and standards, regulations, and project documentation\- Deep third\-party integrations including Procore, Microsoft 365 and Graph API, SharePoint, payroll and accounting systems, contracting data sources, and security tooling\- Well over a hundred scheduled automation jobs handling syncs, monitoring, reporting, and alerting\- A local compute fleet running vector search, model routing, and local inference alongside frontier API modelsThe platform layer is OpenClaw. Agent behavior is defined through configuration and markdown, with Python implementing the tools each agent can call.
Responsibilities
- Design, develop, and refine AI models using frameworks such as TensorFlow and other machine learning tools to solve complex problems.
- Implement natural language processing (NLP) techniques for data extraction and analysis from unstructured data sources.
- Utilize big data systems like Hadoop and Spark to process large datasets efficiently for predictive modeling analysis.
- Collaborate with cross\-functional teams to integrate AI models into cloud\-based platforms utilizing AWS and machine learning cloud services.
- Conduct statistical analysis, model training, evaluation, and validation to ensure high accuracy and robustness of AI solutions.
- Develop scalable data pipelines using ETL processes, Talend, and SQL databases to support ongoing AI initiatives.
- Deploy AI models into production environments with a focus on model evaluation, monitoring, and continuous improvement.
REQUIRED QUALIFICATIONS
OpenClaw or comparable agent framework. Hands\-on experience building agents is required. You should be comfortable with agents and session management, the skills system, tool definitions and tool permission policy, gateway configuration, scheduled agent tasks, agent memory and context files, and sub\-agent orchestration
Production LLM engineering. You have built and shipped LLM\-powered systems that other people depended on, including:
- Tool and function calling, and designing tool interfaces a model can use reliably
- Multi\-step agentic workflows with planning, tool selection, and error recovery
- Context management: deciding what belongs in the context window versus retrieval, and managing the associated cost and latency tradeoffs
- Retrieval\-augmented generation, including chunking strategy, embedding selection, hybrid search, reranking, and honest evaluation of retrieval quality
- Structured output generation and schema validation\-
- Model selection and routing across cost, latency, and capability tradeoffsStrong
- Strong Python skills. You can work confidently in a large existing codebase: reading unfamiliar code, tracing bugs across modules, writing tests, and refactoring safely.
- API integration experience. Much of this work is systems integration rather than model work. You need real proficiency with REST APIs, OAuth 2\.0 and token management, rate limiting, retries, pagination, and idempotency. Critically, you should be someone who verifies API behavior empirically rather than assuming the documentation is complete or current.
- Diagnostic judgment. The hardest problems in this role fail silently rather than loudly: a monitor that reports healthy because it quietly stopped checking, an agent answering confidently from stale data, or a data feed that looks like a slow week but is actually broken. We need an engineer whose instinct is to ask how they know something is genuinely working, and then go prove it.
PREFERRED QUALIFICATIONS
Microsoft 365 and Graph API: mail, calendar, Teams, SharePoint, app registrations, and permission scopes
Vector databases in production, ideally Qdrant, and practical experience with embedding models
Local model hosting and serving: Ollama, vLLM, LiteLLM, and quantization tradeoffs\- Mesh networking such as Tailscale, and basic reverse proxy configuration
PowerShell and Windows endpoint scripting\- Evaluation frameworks for measuring LLM output quality
Front\-end skills for internal dashboards and portals
Vision and OCR work for drawing analysis and scanned document extraction
COMPENSATION AND BENEFITS
$75,000 \- $85,000 \- Compensation is commensurate with experience and will be discussed during the interview process. This is a full\-time position based in Boise, Idaho, with hybrid flexibility possible.MGI Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
Join us in shaping the future of healthcare technology by leveraging cutting\-edge AI innovations that make a real difference in patient safety!
Pay: $75,000\.00 \- $85,000\.00 per year
Benefits:
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Work Location: In person
Salary Context
This $75K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MGI,inc., 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. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $75K to $85K.
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.
MGI,inc. AI Hiring
MGI,inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Meridian, ID, US. Compensation range: $85K - $85K.
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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