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About This Role
Mastronardi Produce pioneered the commercial greenhouse industry in North America, and we’re now the leading greenhouse vegetable company on the continent. Our award\-winning, flavorful produce is packed under the SUNSET® brand and is available at leading grocery retailers across North America. Family owned for over 70 years, we pride ourselves on having the most flavorful products and the best people in the industry. We are constantly pushing boundaries to be a leader in fresh produce innovation. We seek individuals that demonstrate our PRIDE values (Passion, Respect, Innovation, Drive, Excellence) to help us fulfill our mission to inspire healthy living through WOW flavor experiences.
Our corporate office in Livonia Michigan is currently seeking an AI Engineer to join our team. The AI Engineer is responsible for building, deploying, and maintaining AI\-powered applications and integrations that extend Mastronardi’s AI Enablement platform across the business. The role focuses on turning AI capabilities — large language models, retrieval, and agentic workflows — into production\-ready tools that are reliable, governed, and embedded directly into business systems and processes. This is a hands\-on individual contributor role: the AI Engineer writes code, builds integrations, and ships working solutions directly, with accountability for the technical architecture, performance, and security of every solution deployed.
Values:
To perform the job successfully, the incumbent’s behavior must be consistent with the PRIDE values expected of all Mastronardi Produce employees: be Passionate; have Respect; be Innovative; be Driven and strive for Excellence.
Primary Responsibilities:
- Design, build, and deploy AI\-powered applications and integrations that connect large language models (e.g., Claude, Azure OpenAI) to business systems and data sources.
- Build retrieval\-augmented generation (RAG) pipelines and knowledge bases that ground AI outputs in accurate, current company data.
- Develop and refine prompts, system instructions, and evaluation frameworks to ensure AI outputs are accurate, consistent, and auditable.
- Build the initial APIs, connectors, and integration logic between AI platforms and enterprise systems (ERP, WMS, data platforms, Microsoft 365\) as part of solution design, handing off production deployment and ongoing operation to Cloud Operations \& Infrastructure.
- Define the guardrails, audit points, and monitoring requirements AI solutions need so outputs can be reviewed, debugged, and improved after deployment, partnering with Cloud Operations \& Infrastructure on the monitoring infrastructure that implements them.
- Test AI solutions rigorously before release, including functional testing, edge\-case validation, and structured evaluation against real business scenarios.
- Partner with the Data Engineering and Data Governance teams to ensure AI solutions use well\-structured, quality data and comply with security and privacy requirements.
- Stay current on the AI tooling landscape (models, frameworks, agent platforms) and recommend where new capabilities apply to real business problems.
- Build reusable components, templates, and internal libraries that speed up delivery of future AI solutions.
- Create and maintain clear technical documentation for all solutions, architectures, and operating procedures.
- Ensure all solutions align with IT governance, security, and compliance standards.
- Design and maintain evaluation suites and benchmarks to track model and prompt performance over time, catching regressions before they reach production.
- Contribute to architecture decisions on model selection and hosting (e.g., Azure AI Foundry vs. direct API integration), weighing cost, performance, and reliability tradeoffs.
- Support incident response for AI\-related production issues and participate in post\-incident reviews to identify root cause and prevent recurrence.
Education/Background Requirements:
- At least 5 years experience in AI Engineering
- Diploma/Degree in related discipline
- Software development experience in Python (required) and at least one other language (e.g., JavaScript/TypeScript, C\#, or similar).
- Hands\-on experience building applications with large language models — prompt engineering, RAG, tool/function calling, and agentic workflows.
- Experience with LLM platforms and APIs such as Claude (Anthropic), Azure OpenAI, or equivalent.
- Experience building and consuming REST APIs and integrating disparate systems.
Specific Knowledge, Skills and Abilities Required
- Working knowledge of vector databases, embeddings, and retrieval techniques.
- SQL: ability to query, join, and filter data across relational databases.
- Experience with cloud platforms (Microsoft Azure preferred) including AI/ML services such as Azure AI Foundry.
- Version control and CI/CD practices (Git, automated testing, deployment pipelines).
- Understanding of AI safety, governance, and evaluation practices — ensuring outputs are auditable and appropriate for business use.
- Familiarity with ERP or enterprise data structures (Microsoft Dynamics NAV/365, SAP, or equivalent) is an asset.
- Understanding of responsible AI practices, including bias evaluation and hallucination mitigation, to keep model outputs safe and appropriate for business use.
- Experience with containerization and deployment tooling (e.g., Docker, Azure App Service or Functions) is an asset.
Working Conditions:
- Typical office environment.
Please note: Mastronardi Produce has accommodation processes and policies in place and provides accommodation for employees with disabilities. If you require a specific accommodation because of a disability or documented medical need, please contact the Human Resource office so that arrangements can be made for the appropriate accommodation to be put into place.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 Mastronardi Produce, 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.
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.
Mastronardi Produce AI Hiring
Mastronardi Produce has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Livonia, MI, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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