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About This Role
Location: West Palm Beach, FL, US Req ID: 42010 Date: Sep 23, 2026 Florida Crystals Corporation is a fully integrated cane sugar company. Florida Crystals regeneratively farms sugarcane and rice in South Florida, where it owns two sugar mills, a sugar refinery, a packaging and distribution center, Florida's only rice mill, a compost facility, and one of the largest renewable power plants of its kind in the U.S., which uses sugarcane fiber to generate eco-friendly energy that powers its sugar operations. Florida Crystals owns one of the largest Regenerative Organic Certified® farms in the U.S. and its Florida Crystals® products are the only ROC™ sugar grown and milled sugar in the country. Florida Crystals owns ASR Group International, Inc., a holding company that conducts operations through its subsidiaries. The ASR Group® family of companies make up the world’s largest refiner and marketer of cane sugar. Florida Crystals is headquartered in West Palm Beach, Florida. Learn more at www.FloridaCrystalsCorp.com.
OVERVIEW
The AI Integration Solution Owner is a hands-on technical role responsible for designing, building, and operationalizing enterprise AI capabilities across the SAP and Microsoft ecosystems. Reporting to the Director, Infrastructure & Collaboration Architecture, this position leads the technical integration of SAP Business Technology Platform (BTP), SAP Joule, Microsoft Copilot (including Copilot Studio, Microsoft 365 Copilot, and Azure OpenAI) and other AI technologies into Florida Crystals' core business processes. The engineer partners with SAP functional leads, Cybersecurity, Enterprise Architecture, and business stakeholders to deliver AI agents, extensions, and workflow automations that generate measurable value while adhering to the company's AI governance, data protection, and identity standards.
DETAILED ROLES & RESPONSIBILITIES:
- SAP BTP & Joule Integration + Design, build, and deploy AI extensions and integrations on SAP Business Technology Platform (BTP) using Build Code, CAP, Build Process Automation, and SAP AI Core / Generative AI Hub. + Configure and operationalize SAP Joule across S/4HANA, SuccessFactors, Ariba, and other SAP LOB solutions, including Joule Studio custom skills and Joule agent orchestration. + Develop and maintain BTP destinations, connectivity, principal propagation, and API integrations between SAP and non-SAP systems (Microsoft 365, Azure, Snowflake, third-party APIs). + Ground Joule and generative AI responses in enterprise data using SAP HANA Cloud vector engine, Knowledge Graph, and retrieval-augmented generation (RAG) patterns. + Implement Joule and BTP AI use cases across HR (SuccessFactors), Finance, Procurement, Supply Chain, and Manufacturing in partnership with SAP functional teams. + Manage the BTP subaccount, entitlement, and space model for AI workloads, including cost monitoring and consumption optimization.
- Microsoft Copilot & Copilot Studio + Design and build custom agents, topics, plugins, and connectors in Microsoft Copilot Studio for enterprise and department-specific use cases. + Deploy and administer Microsoft 365 Copilot, including licensing strategy, semantic index readiness, SharePoint/OneDrive content governance, and Copilot analytics. + Extend Copilot with declarative agents, API plugins, and Graph connectors that surface SAP, ServiceNow, and other line-of-business data inside Teams, Outlook, and Word. + Integrate Azure OpenAI, Azure AI Foundry, and Azure AI Search into Copilot Studio and custom applications with private endpoints, managed identities, and content filtering. + Partner with the Microsoft 365 team on Purview data classification, DLP, and sensitivity labels required for safe Copilot deployment.
- Cross-Platform AI Engineering + Build integration flows that connect SAP Joule and Microsoft Copilot so that agents can reason across both ecosystems (e.g., Joule invoking Copilot skills and vice versa). + Develop reusable APIs, prompt libraries, function-calling patterns, and agent orchestration templates for use across the enterprise. + Implement CI/CD pipelines for AI artifacts (prompts, agents, extensions) using Azure DevOps, GitHub, and SAP BTP transport tooling. + Instrument AI solutions with observability, evaluation, and feedback loops covering accuracy, latency, cost, hallucination, and user satisfaction. + Troubleshoot production AI issues across identity (Entra ID, IAS/IPS), networking, data grounding, and model behavior.
- AI Governance, Security & Compliance + Implement AI solutions in alignment with the enterprise AI governance framework, responsible AI principles, and Florida Crystals cybersecurity standards. + Enforce identity, access, and data protection controls for AI agents, including least-privilege service principals, secrets management, and audit logging. + Partner with Cybersecurity, Legal, and Privacy on model risk reviews, data residency, prompt/response logging, and third-party AI vendor assessments. + Document AI system architecture, data flows, and control mappings to support internal audit, SOX, and regulatory requirements.
- Collaboration & Enablement + Work closely with SAP functional leads, Enterprise Architecture, Cybersecurity, and the PMO to align AI delivery with the IT roadmap. + Provide technical mentoring to junior developers, citizen developers, and functional analysts building low-code AI solutions. + Produce technical documentation, reference architectures, and enablement materials for IT and business audiences. + Support proofs of concept, vendor evaluations, and RFPs related to AI platforms and services.
WORK EXPERIENCES
- 5+ years of hands-on integration or software engineering experience, with at least 2 years focused on AI, generative AI, or intelligent automation solutions.
- Demonstrated production experience on SAP Business Technology Platform (BTP), including CAP/Node.js or Java, SAP Build, Destinations, and BTP security model.
- Working experience with SAP Joule, Joule Studio, or SAP AI Core / Generative AI Hub, including grounding and custom skill development.
- Hands-on experience building solutions with Microsoft Copilot Studio, Microsoft 365 Copilot extensibility, Power Platform, and/or Azure OpenAI.
- Strong proficiency with REST/OData APIs, JSON, OAuth 2.0, SAML, and enterprise identity providers (Entra ID, SAP IAS/IPS).
- Experience with at least one modern programming language (JavaScript/TypeScript, Python, Java, or ABAP RAP) and with CI/CD tooling (Azure DevOps, GitHub Actions, or SAP CTS+).
- Familiarity with prompt engineering, RAG, vector databases, evaluation frameworks, and responsible AI practices.
- Experience integrating AI with core enterprise systems such as S/4HANA, SuccessFactors, Ariba, ServiceNow, Microsoft 365, or Snowflake.
- Strong troubleshooting skills across integration, identity, networking, and data layers.
- Excellent written and verbal communication skills, including the ability to explain AI concepts to both technical and business audiences.
EDUCATION REQUIREMENTS
- Bachelor's Degree in Computer Science, Information Systems, Engineering, or related field, or equivalent experience.
- Preferred certifications: SAP Certified Development Associate – SAP BTP / CAP, SAP AI Associate, Microsoft Certified: Power Platform Developer Associate, Microsoft Applied Skills for Copilot Studio, Microsoft Azure AI Engineer Associate (AI-102), or Microsoft Certified: Cybersecurity Architect Expert.
ESSENTIAL CAPABILITIES (KNOWLEDGE, SKILLS, ABILITIES AND PERSONAL ATTRIBUTES)
- Deep technical curiosity and a builder's mindset – prefers shipping working integrations over slideware.
- Ability to translate ambiguous business problems into concrete AI and integration designs.
- Strong systems thinking – understands how identity, data, security, and user experience interact in AI solutions.
- Comfortable operating in a hybrid SAP + Microsoft environment and bridging both cultures.
- Self-directed, organized, and able to manage multiple concurrent workstreams with limited oversight.
- Commitment to responsible AI, data privacy, and enterprise security standards.
- Business ethics, uprightness, honesty and sincerity as a basis of trustworthiness.
- Outstanding achievement – continually surpassing standards to achieve/maintain quality.
- Maintains confidentiality and protects sensitive data at all times.
- Adheres to organizational and department specific safety standards and guidelines. Works collaboratively and supports efforts of team members.
- Demonstrates exceptional customer service.
LOCATION OF ROLE
- West Palm Beach, FL (hybrid)
We are an equal opportunity employer. We do not discriminate on the basis of race, color, creed, religion, gender, sexual orientation, gender identity, age, national origin, disability, veteran status or any other category protected under federal, state, or local law. All employment is decided on the basis of qualifications, merit, and business need.
Nearest Major Market: Palm Beach
Nearest Secondary Market: Miami
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At American Sugar Refining, 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 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 $212,000 based on 11,857 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
American Sugar Refining, Inc. AI Hiring
American Sugar Refining, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Palm Beach, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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