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About This Role
Here at Scotts Miracle\-Gro there is no such thing as a typical day. Our culture is constantly energized by new and exciting growth opportunities and at a rapid pace. Below are details on an open job. If the role interests you and you would like to be considered we encourage you to apply!
AI Engineer — SAP S/4HANA \& Joule
About the role
We are migrating to SAP S/4HANA and have a live sandbox ready today. We are hiring a senior AI Engineer who can stand up the AI layer of our SAP landscape from day one — building Joule assistants and agents, designing AI workflows on SAP BTP AI Core / AI Launchpad, and turning SAP Business Data Cloud (BDC) data products into intelligent, agentic experiences. This role not only addresses custom AI and automation needs that arise when core S/4HANA processes are customized, particularly where processes cross multiple platforms (e.g., SAP talking to Kinaxis) but also ensure we are fully leveraging the native AI services offered on top the clean core where applicable. You will partner with functional, data, and security leads to embed AI into core ERP processes while staying true to a fit\-to\-standard implementation.
What you will do:
- Design, build, and operate Joule assistants and Joule agents that automate finance, supply chain, R\&D, procurement, manufacturing, and HR workflows in S/4HANA.
- Build agentic experiences on top of SAP Business Data Cloud (BDC) data products — turning curated data into RAG, decision\-support, and action\-taking agents.
- Architect and deploy AI workflows on SAP BTP using AI Core, AI Launchpad, Generative AI Hub, HANA Cloud Vector Engine, and Build Process Automation.
- Champion a fit\-to\-standard approach: extend SAP cleanly via BTP side\-by\-side extensibility, key\-user tools, and standard APIs rather than core modification.
- Partner with functional and data leads to translate business processes into agent capabilities, tool calls, prompts, and guardrails grounded in SAP master and transactional data.
- Define the reference architecture for prompt engineering, model selection, evaluation, observability, and human\-in\-the\-loop review across the SAP landscape.
- Implement responsible\-AI controls: data classification, PII handling, role\-based access, audit trails, and alignment with SAP's AI ethics and our enterprise policies.
- Lead the AI workstream of the S/4HANA migration — including sandbox experimentation, MVPs, hypercare, and the operating model for ongoing AI delivery.
- Mentor analysts, developers, and functional teams on responsible use of Joule, GenAI, and agent patterns inside SAP.
Required experience (minimum 8 years)
- 8\+ years of overall engineering experience, with at least 3 years building production AI / ML / GenAI solutions in an enterprise setting.
- Hands\-on experience with SAP — S/4HANA (public or private cloud preferred), including a working understanding of fit\-to\-standard ERP delivery and clean\-core principles.
\-Optional \- Demonstrated experience designing and building with SAP Joule — copilots, assistants, and Joule agents — and integrating them with SAP business processes.
- Production experience on SAP BTP, specifically AI Core, AI Launchpad, and Generative AI Hub; comfortable deploying, versioning, and monitoring models and prompts there.
- Working knowledge of SAP Business Data Cloud (BDC) — data products, intelligent data lineage, and how to expose BDC data to AI agents safely.
- Strong software engineering fundamentals in Python and/or Node/TypeScript; experience with REST/OData/GraphQL, event\-driven patterns, and CI/CD.
- Practical experience with LLM application patterns: RAG, function/tool calling, agentic orchestration, evaluation, guardrails, and cost/latency optimization.
- Experience integrating SAP with non\-SAP systems via SAP Integration Suite (CPI), Event Mesh, or equivalent middleware.
- Demonstrated ability to work directly with business stakeholders and translate ambiguous requirements into shippable AI workflows.
Nice to have
- Experience with SAP Datasphere, HANA Cloud (including Vector Engine), and SAP Analytics Cloud.
- Hands\-on with SAP Build (Apps, Code, Process Automation) and CAP / RAP extensibility.
- ABAP / ABAP Cloud, CDS views, and Fiori UX familiarity for embedded AI scenarios.
- Experience deploying agentic frameworks (LangGraph, LlamaIndex, AutoGen, CrewAI) and evaluating them against SAP\-native agent tooling.
- Exposure to MLOps tooling (MLflow, Kubeflow, Vertex AI, Azure ML, AWS Bedrock/SageMaker) and how to bridge them with SAP AI Core.
- SAP certifications (S/4HANA, BTP, AI) or comparable demonstrable expertise.
What success looks like in the first 12 months
- Reference architecture for AI on our S/4HANA \+ BTP \+ BDC stack is documented, reviewed, and adopted.
- At least 3 production\-like Joule agents are live across finance, supply chain, or commercial operations in the sandbox — with measurable cycle\-time or accuracy gains.
- BDC data products are exposed to agents through a governed, reusable pattern; at least one BDC\-backed agent is in production\-like setting.
- An evaluation and observability framework is in place for every AI workflow we ship.
- The team has a repeatable, fit\-to\-standard playbook for moving AI use cases from idea to production on SAP.
The starting budgeted pay range for this role will generally fall between $140,000\.00 \- $164,700\.00 per year. Scotts will consider various factors in determining the actual pay including your skills, qualifications, experience, and geographical location.In addition to the determined base salary, this role is also incentive eligible under our corporate bonus programs.For remote roles where the final candidate resides in Alaska, California, Colorado, Illinois, New York, Oregon or Washington, state required pay thresholds will be factored into base salary.
Here at ScottsMiracle\-Gro, we believe providing an enriching and engaging employee experience is what sets us apart from other organizations. We recognize our employees are so much more than just their job title so we offer programs and benefits that support them in all aspects of their lives. Wondering how we do it? Below is a glimpse of our highlight reel…
- Our *Live Total Health* program provides you with options to align to your personal needs . Selections range from medical, dental and vision coverage for you, your spouse/domestic partner and dependents to an outstanding wellness reimbursement program to an unbelievable 401K match (up to 7\.5%) as well as a 15% discount on company stock and much more
- We know our talent is our most precious asset and your unique development contributes to our organization’s success now and in the future. Career growth at our company is not always a ladder. It’s much more like a rock climbing adventure. Grow through exploration and experiences rather than a predictable linear path.
- We value the importance of family . We provide access to Maven Family Planning and up to $30,000 to accommodate for adoption, fertility and surrogacy .
- Be part of something bigger by joining one of our Employee Resource Groups focusing on diversity and inclusion, family, education and sustainability : Scotts Women’s Network, Scotts Black Employees’ Network, Scotts Veterans Network, Scotts Young Professionals, Scotts Pride Network (GroPride), Scotts Associates for a Greener Earth (SAGE), Scotts Family TREE and our Associate Boards.
- Join a company with a strong belief in giving bac k to the communities where we live and work. We have a shared passion for service and volunteerism and believe participating in community service benefits our communities and strengthens our team.
Not interested in this role? Stay up to date on future opportunities by joining our ScottsMiracle\-Gro and Hawthorne Gardening talent communities.
Scotts is an EEO Employer, dedicated to a culturally diverse, drug free workplace.
EEO/AA Employer/Minority/Female/Disability/Veteran/Sexual Orientation/Gender Identity
Notification to Agencies :
Please note that the Scotts Miracle\-Gro company does not accept unsolicited resumes from recruiters or employment agencies. In the absence of a signed Master Service Agreement, and specific approval to submit resumes to an approved requisition, the Scotts Miracle\-Gro company will not consider or approve payment regarding recruiter fees or referral compensations.
Salary Context
This $140K-$164K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Scotts Miracle-Gro, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($152K) sits 16% below the category median. Disclosed range: $140K to $164K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Scotts Miracle-Gro AI Hiring
Scotts Miracle-Gro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Marysville, OH, US. Compensation range: $164K - $164K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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