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About This Role
Ocean Spray is hiring for a(n) Sr. Manager, AI Enablement! We’re a team of farmers, thinkers, creators, and doers. Whatever your title, whatever your role — it always comes back to this: we’re a farmer\-owned co\-op where everyone rolls up their sleeves to get the job done. Three maverick farmers started it all — and we’ve been making our own way ever since.Position Location: We're all about flexibility! This will be a *hybrid role* based out of our corporate headquarters in Lakeville, MA or our Seaport Boston, MA location with Mondays and Fridays remote.
The Senior Manager, Artificial Intelligence leads the development, execution, and ongoing evolution of Ocean Spray’s enterprise AI strategy. As Ocean Spray is in the early stages of its AI journey, this role plays a critical part in driving initial awareness, adoption, and value, particularly through Microsoft Copilot, SAP and other AI tools. This role oversees the full AI portfolio, identifying and prioritizing high‑value use cases that deliver measurable business impact while building the foundational processes and capabilities the cooperative needs for long\-term growth. Operating at the Leading Others level, this leader manages a small team of AI practitioners and works closely with IT, business functions, and external partners to design and deploy AI solutions that leverage enterprise applications, tools, and data. The role ensures all AI initiatives align with business goals, follow responsible AI practices, and support organizational transformation.A Day in the Life...
AI Strategy \& Portfolio Leadership
- Develop and refine the enterprise AI strategy in alignment with cooperative business priorities.
- Identify, assess, and prioritize high‑impact AI opportunities across Commercial, Operations, Supply Chain, Finance, Corporate, and IT.
- Build business cases that quantify expected outcomes, ROI, risks, and success metrics.
- Lead execution of the AI portfolio to ensure delivery of measurable value.
- Strengthen AI governance, security, and responsible AI practices.
- Provide clear updates, portfolio reporting, and executive communications.
Technical Leadership \& Delivery
- Partner with Data Engineering, Cloud/Infrastructure, Enterprise Applications, Data Governance, and Cybersecurity teams to architect scalable AI solutions.
- Contribute hands‑on to solution design, prototyping, model evaluation, and prompt engineering.
- Support implementation of MLOps and model lifecycle practices with platform teams.
- Ensure operationalization, monitoring, and performance optimization of AI solutions.
- Provide technical direction to data scientists, ML engineers, IT product owners, and other contributors.
Cross‑Functional Collaboration \& Adoption
- Serve as a champion for AI awareness and adoption across the cooperative, especially focusing on embedding Microsoft Copilot, SAP, Workday and other approved tools into processes and workflows.
- Support change management efforts to drive adoption of AI\-enabled improvements.
- Build strong partnerships with internal stakeholders and external vendors to introduce leading tools and methods.
- Foster a collaborative, learning\-oriented team environment.
What We Are Looking For:
Technical Skills
- Strong understanding of AI/ML concepts (supervised/unsupervised learning, NLP, generative AI, optimization, predictive modeling).
- Ability to prototype or evaluate AI models and prompts using tools such as Python and SQL.
- Familiarity with enterprise AI platforms, cloud technologies, MLOps, and responsible AI frameworks.
- Solid knowledge of data engineering fundamentals, data quality, and integration.
- Experience with enterprise systems such as SAP S/4HANA, SAP IBP/SAC, Microsoft 365 Copilot, Maximo or SAP EAM/APM, Workday, and Azure services.
Strategic \& Analytical Skills
- Strong ability to evaluate business needs and size opportunities.
- Skilled at translating complex technical concepts into business\-friendly language.
- Capable of managing complex initiatives while balancing short‑ and long‑term priorities.
Leadership \& Collaboration
- Proven ability to lead, coach, and develop AI talent.
- Strong cross\-functional influence and stakeholder management skills.
- Sound decision\-making and adaptability in dynamic environments.
Communication \& Change Leadership
- Excellent written and verbal communication skills for technical and executive audiences.
- Able to articulate value, risks, progress, and recommendations clearly.
- Experience supporting change management related to AI adoption.
Education:
Bachelor's or University Degree (Required)Work Experience:
At least 7 Years of ExperienceAnnual Salary:
$128,000 \- $160,000*The base salary range information above serves as a guideline of the position’s typical hiring range. We value and appreciate what makes you unique and will consider a variety of factors when determining an offer. These factors include, but are not limited to, your skills and experience, external and internal benchmarks, as well as overall company considerations. Certain positions may be eligible for short\-term and long\-term incentive rewards. We also offer a competitive and comprehensive benefits program that supports all aspects of your health and well\-being.*
Benefits:
- Complete insurance package on Day\-1 that includes a plethora of health and wellness programs
- + Health, Dental and Vision insurance
+ Health savings account
+ Flexible spending account
+ Life and accident insurance
+ Employee assistance program
+ Telehealth services
+ 1:1 health coaching
+ Supportive benefits for all the stages of your life
- 401(k) with up to 6% Company matching; additional potential discretionary match at year\-end
- Short\-Term Incentive/Performance bonuses
- Flexible scheduling options
- Vacation pay, up to three weeks of time (pro\-rated for your first year of employment)
- Holiday pay for 12 holidays
- Career development and growth opportunities
- Tuition/Education assistance programs
- Access to LinkedIn Learning
- Scholarship programs for children of employees
- Parental leave
- Bright Horizons Family Solutions – Back\-up care, tutoring, etc.
- Adoption assistance
- Bereavement leave
- Up to $300 fitness reimbursement
- Up to $300 massage reimbursement
- Employee appreciation events
- Employee discounts
- Charitable giving
Who We Are:
You might have our iconic cranberry juice in your fridge or have gotten into heated holiday debate about what’s better \- canned or fresh cranberry sauce. But did you know that the hardworking people growing the superfruit in our products are 700 family farmers that own our cooperative? They entrust us with what is most precious to them to create new and innovative products that will delight consumers and grow this beloved brand today and into the future.
Team members, farmers, consumers and communities alike\-we value what makes us unique and strive to connect our farms to families for a better life by living our values:
- Grower Mindset – We embrace our grower\-owners innovative spirit and heritage through confidence, learning and focus on the future.
- Sustainable Results – Guided by purpose, we are focused on delivering results for our grower\-owners.
- Integrity Above All – We are ethical, doing the right thing for our grower\-owners, customers, consumers and each other
- Inclusive Teamwork – We build diverse and inclusive teams that strengthen our cooperative.
*All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.*
*For MA Applicants* *–* *It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. Any employer who violates this law shall be subject to criminal penalties and civil liability.*
Salary Context
This $128K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ocean Spray, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($144K) sits 34% below the category median. Disclosed range: $128K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ocean Spray AI Hiring
Ocean Spray has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Middleboro, MA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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