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About This Role
Job Description
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Summary
A Maintenance Director is responsible for managing and directing the maintenance department.
This includes planning, coordinating, and supervising both preventive and unplanned maintenance activities for the facilities, freezers, machinery, and equipment. This role involves strategic planning and implementation of maintenance programs that uphold safety standards and operational efficiency. The director coordinates the activities of the maintenance team, manages departmental budgets, and ensures compliance with regulatory requirements. The Maintenance Director collaborates with other department heads to align maintenance schedules with organizational goals and oversees capital improvement projects and vendor relationships.
Positions Supervised
Engineering Technicians
Maintenance Supervisor/Manager and Maintenance Purchasing Administrator
Essential Duties and Responsibilities
1\. Develop and implement a comprehensive preventive maintenance program for all facilities and equipment to ensure operational readiness.
2\. Formulate and manage the annual maintenance budget for all facilities, including forecasting expenses and justifying capital expenditure requests.
3\. Prepares and controls capital projects within budget constraints to meet departmental goals, assuring accurate cost analyses and project completion dates.
4\. Monitors, trouble\-shoots, and tests of new methods and procedures and/or newly installed equipment to ensure fulfillment of contract specifications and system efficiencies. Assists with programming changes required as plant requirements dictate.
5\. Negotiate and oversee contracts with external vendors and service providers for specialized maintenance, repairs, and construction projects.
6\. Reviews and provide recommendations for changes to mechanical drawings and process documentation.
7\. Conducts a continuous search for new materials, processes, and procedures to enhance and improve the product line. Interfaces with internal resources and external vendors to evaluate, cost estimate, and select purchase parts.
8\. Oversees the preventive maintenance program to maintain production machinery and equipment in proper working condition. Administer a computerized maintenance management system to track work orders, asset history, and resource allocation.
9\. Ensure departmental compliance with all federal, state, and local regulations, including occupational safety and environmental standards.
10\. Inspects jobs in process and at completion to ensure that standards of quality, workmanship, and safety are met.
11\. Manage the inventory of spare parts, tools, and essential supplies to minimize downtime and control operational costs.
12\. Maintains solid working knowledge of all regulatory requirements relating to wastewater, air quality (emissions), diesel conversion, and other applicable regulations. Monitors compliance, submit all proper documentation and schedules maintenance and repair for all companies and facilities.
13\. Coordinate with other department heads to schedule maintenance activities in a way that minimizes disruption to overall operations.
14\. The director participates in the weekly senior staff management meeting. Assures effective communication between departments and key personnel. Provides ongoing input to the Vice President Operations and the CEO \&President, especially regarding unusual or serious situations.
15\. Delegate responsibility and achieves results through subordinates; maintains order in an environment of changing priorities.
16\. Responsible for immediate supervision of departmental staff, including hiring, performance, and disciplinary determinations; conducts staff meetings and initiates wage increases based on meritorious performance; hears and resolves complaints, problems, grievances. Provides training opportunities and skill growth for staff. Maintains appropriate levels of employee relations and morale.
17\. Attends meetings, conferences, and seminars requiring periodic travel.
18\. Other duties as assigned.
19\. Regular attendance during established business hours is an essential requirement of the position.
Qualification Requirements
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
Education and/or Experience
- B.S. degree in facilities management, engineering or business administration equivalent from an accredited college or university.
- Five years' experience in increasingly responsible positions.
- Sound knowledge and demonstrated ability in maintenance principles and disciplines, employed manufacturing processes, technical report preparation, and meeting deadlines.
- Knowledgeable of building codes and safety regulations.
- Considerable skill in arriving at cost estimates on complex projects.
- Technical and administrative aspects of all phases of building inspections, building codes and city ordinances; contractor licenses; regulations and guidelines prepared by state and federal agencies relative to plant inspection requirements; and general construction techniques.
- Sound knowledge and experience of the principles and practices of business administration, including personnel practices, employment laws, and fiscal management practices; and the appropriate methods and means of dealing with human behavior situations in a variety of circumstances.
- Leadership skills
- Demonstrated initiative and ability to think creatively to identify business opportunities
- Ability to recruit, train, and motivate personnel to balance staffing strength with profitability and growth.
- PC\-based computing experience (word processing\-Word, spreadsheet\-Excel, email\-Outlook). Proficiency in CAD software (AutoCAD) and PLC programming.
- An equivalent combination of experience and training that provides the required knowledge, skills, and abilities.
Language Skills
- Ability to read and comprehend English and comprehend company policy and state and federal regulations as well as safety rules, operating and maintenance instructions, and procedure manuals.
- Ability to persuade and negotiate conflicts and problems.
- Ability to identify and resolve administrative problems.
- Ability to speak effectively before groups of customers or employees of the organization.
- Ability to relate well with all levels of MFP staff, growers, customers, and other businesspeople. Because of considerable interaction with all groups, effective language and human relations skills are essential.
- Professional writing skills to compose memos, letters and other business correspondence, observing all rules of grammar, spelling, and punctuation.
Mathematical Skills
- Ability to create, review and understand departmental budgets.
- Ability to work with mathematical concepts such as probability and statistical inference, and fundamentals of plane and solid geometry and trigonometry.
- Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations.
- Ability to review cost data, analyze and make recommendations.
- Ability to apply mathematical operations to such tasks as frequency distribution, determination of test reliability and validity, analysis of variance, correlation techniques, sampling theory, and factor analysis.
Reasoning Ability
- Ability to assess operational, program, staffing, and fiscal needs.
- Ability to research, assess and apply appropriate laws, rules, and regulations to the work environment
- Ability to make decisions, exhibit sound and accurate judgment and make timely decisions.
- Ability to manage details and comprehend the full scope of the Engineering/Maintenance department.
- Ability to solve practical problems and deal with a variety of concrete variables in situations where only limited standardization exists.
- Ability to interpret a variety of instructions furnished in written, oral, diagram, or schedule form.
- Ability to define problems, collect data, establish facts, and draw valid conclusions.
- Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with abstract and concrete variables.
- Ability to coordinate materials and schedules to meet deadlines and budget goals.
Environmental Conditions
- Indoors in an office environment (40%) and in and out of doors in the processing plant (60%).
- Potential workplace hazards in the laboratory and processing plant. All safety regulations and use of PPE’s will be followed.
- Works long and irregular hours and under pressure conditions
- Frequently work at a fast pace with unscheduled interruptions.
- Manages multiple deadlines.
Company Description
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With a reputation built on our employee’s commitment to outstanding product quality and customer service, Milne utilizes innovative technologies and rigorous quality controls to provide healthy fruit products that are 100% natural to meet the needs of a diverse consumer marketplace. Founded in 1955, Milne Fruit Products is the industry leader in creating fruit juices, concentrates, purees and custom blends for the industrial food and beverage markets.
Benefits
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- Medical Insurance
- Dental Insurance
- Vision Insurance
- 401(k), after 12 month eligibility period
- Life insurance
- Short\-term Disability
- Long\-term Disability
- Paid Vacation
- Sick Pay
- Holiday Pay
- Continuing education program
- Bonus Eligible
Salary Context
This $145K-$165K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Milne Fruit, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($155K) sits 7% below the category median. Disclosed range: $145K to $165K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Milne Fruit AI Hiring
Milne Fruit has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Prosser, WA, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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