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About This Role
The Manager, AI & Business Transformation leads Fresh Origins’ AI program from the business side. This is a business analyst and project manager first: someone who can sit with Operations, Sales, Accounting, and Production, understand how work actually gets done, find where AI and automation will pay off, and then run the projects that deliver it. The core of the role is AI program leadership: building the roadmap, managing outside AI partners, running pilots, driving adoption of the company’s enterprise AI tools, and reporting results to leadership and corporate. Supporting that is business analytics and reporting: defining KPIs, partnering with the analytics team on the company data lake and Power BI, and making sure decisions run on one trusted set of numbers. Underneath both is the ERP: understanding the data model, working with the ERP administrator and implementation partner on configuration and integration needs, and keeping AI and analytics initiatives grounded in the system of record.
Reporting to the IT Director, this position is the operational leader of Fresh Origins’ AI and data initiatives and a core part of the company’s investment in AI-driven transformation. The role works closely with the EDI & Production Systems Coordinator, who manages EDI transactions, trading partner platforms, VIDA18 warehouse management, item data, and the commercial customer portal, and with stakeholders across Finance, Operations, Sales, and Production. It supports Fresh Origins’ mission of delivering the highest-quality microgreens and edible flowers through innovation and precision.
KEY RESPONSIBILITIES
AI Program Leadership
- Own the AI roadmap and project portfolio: prioritize initiatives by business value, feasibility, cost, and timeline; sequence them; and manage them end to end from discovery through adoption.
- Educate leaders and key stakeholders on AI principles, capabilities, risks, and practical business applications; translate technical concepts into clear business recommendations.
- Lead business analysis for AI use cases: interview stakeholders, map current processes, quantify manual effort and data breaks, and define requirements and success metrics before any tool is selected.
- Run projects as the project manager of record: scope, timeline, resourcing, risk, testing, rollout, and status reporting for each AI and automation initiative.
- Manage outside AI consultants and implementation partners day to day: set deliverables, review work, test outputs, and hold partners to scope, budget, and timeline.
- Lead the AI-driven demand planning initiative from the business side: define the planning questions, coordinate data readiness (planting, harvest, yield, weather, holidays, sales), and validate model outputs with Production and Sales.
- Support the rollout of AI-assisted order intake and customer service automation: process design, user acceptance testing, and clean handoff into ERP and customer-facing workflows in coordination with the EDI & Production Systems Coordinator.
- Drive adoption of the company’s enterprise AI tools: identify use cases by department, deliver training and office hours, manage licenses, and track utilization and outcomes.
- Pilot and evaluate AI capabilities for forecasting, anomaly detection, document processing, and workflow automation; recommend build, buy, or partner decisions.
- Prepare AI program updates for company leadership and corporate stakeholders, including portfolio-level AI reporting and best-practice sharing.
- Maintain the AI governance framework with the IT Director: responsible use, data privacy, vendor risk, and change management.
- Own business continuity for AI tools, automations, and integrations once live: documentation, monitoring, fallback procedures, and clear ownership of what happens when a system fails.
Business Analytics & Reporting
- Define and maintain KPI definitions with Finance, Operations, and Sales so leadership reports from a single source of truth.
- Partner with the analytics team and outside partners on the company data lake and Power BI: prioritize dashboards, validate data, and drive adoption of governed reporting over manually built spreadsheets.
- Analyze structured and unstructured data (transactions, customer service notes, emails, call records) to surface trends, exceptions, and opportunities that feed the AI roadmap and leadership decisions.
- Identify where teams are manually assembling data the data lake already produces and migrate them to governed reporting.
ERP & Systems
- Understand the ERP data model and core processes (order-to-cash, procure-to-pay, inventory, financial close) well enough to design AI and analytics solutions that fit how the business runs.
- Translate business requirements into configuration and integration requests; work with the ERP administrator and implementation partner to deliver them.
- Improve data connectivity across the business systems stack (NetSuite, Salesforce, Power BI, ADP, VIDA18, and EDI middleware) so AI and analytics initiatives run on connected, trusted data.
- Champion master data quality across items, customers, vendors, and GL structure; lead cleanup and reclassification efforts that unblock reporting and AI.
- Review ERP release notes and platform AI features; recommend adoption to leadership.
- Document processes, data standards, and standard operating procedures for AI-supported workflows.
QUALIFICATIONS & EXPERIENCE
Education
- Bachelor’s degree in Business Administration, Information Systems, Data Analytics, Industrial Engineering, or a related field, or equivalent experience.
- PMP, CBAP, or similar project or business analysis certification is a plus.
Experience
- 5+ years as a business analyst, systems analyst, or project manager in an ERP-driven environment (NetSuite, SAP, Dynamics, Oracle, or similar).
- Proven record leading cross-functional projects end to end and managing vendors, consultants, or implementation partners.
- Hands-on experience gathering requirements, mapping processes, and defining KPIs for operations, finance, or sales teams.
- 1+ years leading or contributing to AI, machine learning, or intelligent automation initiatives in a business setting; experience rolling out enterprise generative AI tools is strongly preferred.
- Experience working with a BI platform (Power BI preferred) and a data warehouse or data lake.
- Experience in food production, agriculture, manufacturing, or distribution is a significant plus.
Technical Skills
- Working knowledge of ERP data structures and core transaction flows; NetSuite experience is a plus, not a requirement.
- SQL for data validation and analysis; comfort with a cloud data warehouse (BigQuery or similar).
- Power BI or equivalent BI tool; dashboard design and KPI definition.
- Project management tools and methods (Agile or hybrid); requirements and process documentation.
- Familiarity with generative AI platforms, AI-powered analytics, and automation tools; ability to evaluate vendors and pilot solutions.
- Advanced Excel (pivot tables, Power Query, XLOOKUP); exposure to Python or scripting is a plus.
CORE COMPETENCIES
- Business-first thinker: starts with the problem and the process, not the tool.
- Strong project management discipline: scopes clearly, communicates status, manages risk, and finishes.
- Clear communicator and teacher; explains AI to non-technical stakeholders, including executives and corporate, without hype.
- Data-literate and analytical; comfortable questioning numbers and validating results.
- Collaborative and pragmatic; builds trust across Accounting, Operations, and Sales.
- Curious and self-directed, with a growth mindset toward AI and automation.
- Organized and accountable; manages multiple vendors, projects, and deadlines at once.
LEADERSHIP EXPECTATIONS
- Act as the trusted advisor to leadership on AI, analytics, and business systems.
- Own outcomes, not just tasks: define success for each initiative, measure it, and report it.
- Manage outside partners and consultants to deliverables, budget, and timeline.
- Mentor the EDI & Production Systems Coordinator and other team members on analysis, data standards, and AI tools; potential future supervision of analyst-level staff.
- Champion responsible AI adoption and data literacy across the organization.
- Model professionalism, accountability, and continuous learning.
- Prioritize the safety and well-being of co-workers by following all company safety protocols.
PERFORMANCE MEASURES
- On-time, on-scope delivery of AI and automation projects (demand planning, AI order intake, tool rollouts).
- Measurable time savings, cost reduction, or accuracy gains from AI initiatives, documented per project.
- Enterprise AI tool utilization and active use cases by department.
- Adoption of governed Power BI reporting and reduction in manually built spreadsheets.
- Quality of requirements, process documentation, and KPI definitions.
- Partner and vendor performance against scope, budget, and timeline.
- Stakeholder satisfaction across Finance, Operations, and Sales.
CAREER DEVELOPMENT OPPORTUNITIES
This position offers growth into roles such as Senior Manager, AI & Transformation; Director of Business Systems; Director of Data & Analytics; or Vice President of AI, Transformation & Strategic Initiatives.
WORKING CONDITIONS
- Standard business hours with extended support during project go-lives and key business cycles.
- Primarily on-site at Fresh Origins’ San Marcos, CA facilities.
- Office environment with regular visits to greenhouse, production floor, cold storage, and warehouse areas to observe processes and work with users.
- Routine use of computer, ERP and BI software, phone, and standard office equipment.
- Occasional after-hours work for rollouts or vendor coordination.
Salary Context
This $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At FIT Solutions, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $100K to $120K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
FIT Solutions AI Hiring
FIT Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Marcos, CA, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
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