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About This Role
SUMMARY: The AI Engineer makes Saturn's data decision-ready. The role builds one reliable data foundation across SAP S/4HANA, Ignition, Excel, and external market data, automates the reporting produced from it, and applies AI so that management receives analysis and recommendations directly, without the manual collecting, reconciling, and formatting steps in between.
The role reports to the CFO and works with the Executive and Plant Leadership Teams. It is a hands-on position: the incumbent builds the data models, reports, and AI solutions personally and is accountable for their accuracy. The role also makes the wider organization AI-ready by raising data quality at the source, building skills across the functions, and setting the rules for responsible use of data and AI. Essential Duties and Responsibilities: include the following. Other duties may be assigned.Data Quality and the Data Foundation* Make high-quality base data an organizational standard: define entry rules, master data discipline, and data quality measurement together with the functions, which remain accountable for the accuracy of the data they create.
- Build and operate one data foundation that connects SAP S/4HANA, Ignition and production systems, Excel-based sources, and external market data such as Nielsen, as a Single Source of Truth.
- Establish consistent KPI and master data definitions and documented calculation logic, so that every report and every AI answer uses the same numbers.
- Work with the functions to eliminate the causes of poor data rather than correcting data downstream.
Automated Reporting for Decision-Making* Automate the recurring executive and plant reporting so that management information is produced without manual collecting, reconciling, or formatting.
- Inventory existing reporting, consolidate duplicates, and retire the manual spreadsheets that automated reporting replaces.
- Deliver executive-level output: few, clear views showing performance, drivers, and recommended action, so that leadership receives decisions to make rather than data to process.
- Use AI to generate commentary, variance explanations, and meeting-ready summaries from the underlying data, with human review before release.
Forward-Looking Analytics* Move reporting from describing the past to anticipating the future: forecasting, scenario and sensitivity analysis, and early-warning indicators.
- Apply AI and statistical methods across all available data - financial, operational, machine, quality, and market - to identify trends, anomalies, and predictive maintenance opportunities.
- Quantify the financial impact of operational trends so that leadership sees the P&L consequence, not only the metric.
AI Solutions and Automation* Identify, prioritize, and implement AI and automation use cases across all functions, each with a business case and a measured benefit.
- Build and maintain AI agents and automated workflows that connect to Saturn's systems and perform real work, rather than standalone chat usage.
- Test and monitor the quality of AI output, and keep a human accountable for every decision.
Making the Organization AI-Ready* Build data and AI capability across the organization: train and coach employees at all levels in the practical, day-to-day use of AI, reporting, and automation tools.
- Identify where AI changes how work is done, and lead the change so that new ways of working are adopted and the replaced routines are retired.
- Make company knowledge and process documentation structured, current, and retrievable, so that both employees and AI systems can use it.
- Set and maintain the rules for responsible use of data and AI, aligned with heristo group policy and recognized frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, including protection of customer- and partner-confidential data from external AI services.
ERP and Process Optimization* Support the optimization of SAP S/4HANA and related business processes, ensuring that data, reporting, AI, and automation requirements are built into the system landscape.
- Partner with KAM serv.it, PMO IT saturn petcare, functional teams, and external providers as the business-to-IT translator, and support process mapping and process mining.
Supervisory Responsibilities: NoneQualifications: 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 accommodations may be made to enable individuals with disabilities to perform the essential functions.Education and/or Experience:* Bachelor's degree in Data Science, Business Analytics, Information Systems, Computer Science, Engineering, Business Administration, or a related field; master's degree preferred. Equivalent demonstrated capability may be considered.
- 5+ years of hands-on experience in data analytics, business intelligence, or data engineering, including personally building and operating reporting solutions; manufacturing experience strongly preferred.
- Demonstrated experience automating recurring reporting and replacing manual spreadsheet processes.
- Practical experience delivering AI or automation solutions into productive use, not pilots alone.
- Proven ability to translate business and operational needs into working solutions with measurable business impact.
- Strong communication, training, and change-management skills, including the ability to present to executives. Candidates should be prepared to present examples of their own work.
Computer and Technology Skills - Required:* SQL at a working level: able to query, model, and transform data independently.
- Advanced Excel and advanced proficiency in a modern business intelligence platform (Power BI preferred; SAP Analytics Cloud an advantage), including data modeling.
- Practical experience extracting and modeling data from an ERP system, preferably SAP S/4HANA.
- Hands-on application of generative AI in a business setting: AI agents, workflow automation, retrieval over company documents, and the judgment to know where AI should and should not be used.
Computer and Technology Skills - Preferred:* Python, or an equivalent language, for data processing and automation.
- Industrial and machine data: Ignition, historians, MES, OPC-UA or MQTT.
- A modern data platform such as Microsoft Fabric, Databricks, Snowflake, SAP Datasphere, or SAP Business Data Cloud.
- Statistical forecasting or machine learning; process mining tools such as Signavio or Celonis; familiarity with AI governance frameworks.
Language Skills: Ability to read and interpret documents such as safety rules, operating and maintenance instructions and procedural manuals. Ability to write routine reports and correspondence. Ability to speak effectively before groups of customers or employees of the organization. German language speaking skills is another advantage. Mathematical Skills: Ability to work with mathematical concepts such as probability and statistical inference, and ability to apply concepts such as: fractions, percentages, ratios and proportions to practical situations. Reasoning Ability: 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. Physical Demands: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of the job, the employee is regularly required to use hands to finger, handle, or feel and reach with hands and arms. The employee is required to stand, walk, sit, and talk and hear. The employee must regularly lift and / or move up to 15 pounds, frequently lift and / or move up to 30 pounds and occasionally lift and / or move up to 50 pounds. Specific vision abilities required by this job include close vision, distant vision, color vision, peripheral vision, depth perception and ability to adjust focus. Allergen Awareness Products produced in this facility may contain the following allergens: Egg, Shellfish, Fish, Gluten. Disclaimer: The above statements are intended to describe the general nature and level of work being performed by employees assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. Job descriptions are not intended to create a contract, nor are they to be construed to constitute contractual obligations of any kind or a contract of employment between Saturn Petcare Inc. and any of its employees. The provisions of job descriptions have been developed at the discretion of management and may be amended or cancelled at any time by Saturn Petcare Inc. with or without notice.
Employment with Saturn Petcare Inc. is voluntarily entered, and the employee is free to resign at will at any time, with or without cause. Similarly, Saturn Petcare Inc. may terminate the employment relationship at will at any time, with or without notice or cause, so long as there is not violation of applicable federal or state law.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Saturn petcare, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Saturn petcare AI Hiring
Saturn petcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Terre Haute, IN, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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