Interested in this AI/ML Engineer role at Dairy Farmers of America, Inc.?
Apply Now →About This Role
Serve as a senior\-level technical leader within IT Solutions Delivery, responsible for deploying, operating, and continuously improving production AI\-enabled platforms and services that support critical business applications.
This role ensures that AI/ML capabilities are delivered into production environments using the same operational rigor, reliability standards, and support models as enterprise IT infrastructure, enabling consistent uptime, performance, and scalability. The engineer partners closely with application teams, platform engineering, and IT operations to ensure AI services are production\-ready, supportable, and aligned to enterprise operational standards.
Job Duties and Responsibilities:
- Deploy AI/ML solutions into enterprise production environments using repeatable, low\-risk release processes
- Build and maintain automated pipelines that support solution delivery across development, testing, and production
- Ensure all AI services meet enterprise standards for deployment, configuration, and change management
- Own day\-to\-day operations of AI\-enabled platforms, ensuring availability, reliability, and performance of business\-facing services
- Establish and enforce site reliability engineering (SRE) practices, including high availability and fault tolerance, capacity planning and auto\-scaling, and redundancy and failover strategies
- Continuously optimize platform performance, resource utilization, and cost efficiency
- Implement and maintain monitoring, logging, and alerting aligned to enterprise ITOM practices
- Define and track service health, performance, and data quality metrics for AI\-enabled services
- Configure proactive alerting for degradations or anomalies and integrate with enterprise event management platforms
- Develop and maintain operational dashboards and visibility tools for ongoing service assurance
- Provide production support for AI\-enabled services, including incident triage and resolution, performing root cause analysis (RCA), and implementing corrective and preventative actions
- Develop and maintain runbooks and support procedures to enable consistent issue resolution
- Partner with operations and service desk teams to ensure support readiness and knowledge transfer
- Integrate AI services into enterprise application and infrastructure ecosystems, ensuring compatibility with existing platforms
- Collaborate with solution delivery, infrastructure, and application teams to ensure services are fully operationalized, properly monitored, and supportable through standard IT processes
- Ensure AI components behave as first\-class enterprise services within the broader application landscape
- Ensure all AI services are deployed and operated in alignment with enterprise security, compliance, and data protection standards
- Manage the full operational lifecycle of AI\-enabled services, including versioning and controlled releases, performance tuning and optimization, and continuous improvement of deployment and support processes
- Identify opportunities to automate and standardize platform operations to improve efficiency and reliability
- Serve as a subject matter expert in AI platform operations
- Lead or support resolution of major incidents and complex operational challenges
- Drive adoption of standardized operational practices, including runbooks, and reliability engineering
- Provide guidance to project teams to ensure solutions are designed for production support from day one
Requirements:
Education and Experience
- Undergraduate degree in computer science, information technology, or related curriculum (or equivalent combination of experience and education)
- 8 or more years of information technology, cloud or platform engineering, DevOps, site reliability engineering (SRE), infrastructure engineering, application operations, or related experience that includes experience:
- + supporting AI/ML platforms, machine learning operations (MLOps), AI\-enabled applications, large\-scale data platforms, or other advanced analytics environments in production
+ deploying, monitoring, and supporting business\-critical applications in cloud\-based and hybrid enterprise environments
+ designing and managing CI/CD pipelines, automated deployment processes, and infrastructure\-as\-code solutions
+ partnering with application development, infrastructure, security, and operations teams to operationalize new technologies and services
+ serving as a technical lead, senior engineer, or escalation point for complex production issues
- Certification and/or License – may be required during course of employment
Knowledge, Skills, and Abilities
- Deep understanding of managing supported systems in a large\-scale environment
- Solid understanding of AI/ML operational practices, including model deployment, model monitoring, inference services, version management, and AI platform lifecycle management
- Strong understanding of backup technologies and cloud technologies
- Strong scripting and automation skills
- Strong collaboration skills with application development, platform engineering, cybersecurity, infrastructure, and service desk teams
- Strong problem solving and analytical skills with the ability to quickly isolate problems, collect data, establish facts, and draw valid conclusions; able to perform root cause analysis and implement sustainable corrective and preventive actions
- Able to deploy and maintain highly available, scalable, and supportable AI\-enabled services in production environments
- Able to automate operational processes, platform provisioning, deployments, monitoring, and recovery activities
- Able to serve as the senior technical escalation point for critical incidents and complex operational challenges
- Able to influence teams and drive adoption of enterprise operational standards and best practices
- Able to communicate complex technical concepts to both technical and non\-technical stakeholders
- Able to prioritize multiple operational demands in fast\-paced production environments
- Able to work independently with limited direction while maintaining accountability for enterprise\-critical service
- Must be able to read, write and speak English
An Equal Opportunity Employer including Disabled/Veterans
Compensation: $98000\-$139000/Annually
Salary Context
This $98K-$139K 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 Dairy Farmers of America, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($118K) sits 45% below the category median. Disclosed range: $98K to $139K.
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.
Dairy Farmers of America, Inc. AI Hiring
Dairy Farmers of America, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kansas City, KS, US. Compensation range: $139K - $139K.
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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