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About This Role
ABOUT WORLD’S FINEST CHOCOLATE: World’s Finest Chocolate does more than make delicious
chocolate. Since 1949, we’ve helped our fundraising customers raise over $4,600,000,000! These
funds are used to buy playgrounds, pay for reading specialists, purchase musical instruments, fund field trips, and more. Our employees work as a team to support our company’s mission: To Deliver Extraordinary Value with Fun \& Purpose.
POSITION OVERVIEW: We are seeking a Sr. AI Solutions Engineer to design, build, integrate, and operationalize practical AI solutions that improve productivity, automate repetitive work, and strengthen decision\-making. This role translates business needs into secure, scalable, measurable AI\-enabled solutions using Snowflake, Microsoft Copilot, Copilot Studio, Azure AI, Power Platform, enterprise data, workflow automation, and integration patterns across core platforms. Snowflake is the central data platform, and deep hands\-on experience is essential to this role.
The role is hands\-on and delivery\-oriented, operating at the intersection of business process improvement, enterprise data, application integration, responsible AI governance, and end\-user adoption. Claude AI may be used as a governed complement to the Microsoft\-first AI stack.
This role balances:
- Practical AI solution design and delivery
- Copilot, agentic AI, workflow automation, and targeted Claude enablement
- Enterprise data, RAG, knowledge grounding, and system integration support
- Responsible AI governance, security, quality assurance, adoption, and operational support
KEY RESPONSIBILITIES:
AI Solution Design \& Delivery
- Identify, prioritize, prototype, and deploy AI use cases across all WFC functional areas
- Translate process pain points into AI solution designs, MVPs, production\-ready capabilities, and measurable outcomes.
- Build reusable automations and documentation standards that can scale across departments.
Copilot, Agents, \& Workflow Automation
- Design and support copilots, assistants, and agents using Microsoft Copilot Studio, Microsoft 365 Copilot extensibility, Azure AI, Power Automate, and related tools, including Claude when appropriate.
- Automate document review, knowledge retrieval, inquiry routing, response drafting, reporting support, workflow initiation, and task summarization.
- Integrate AI\-enabled workflows with Teams, SharePoint, Outlook, Power Platform, Salesforce, SAP, Snowflake, and Power BI while preserving system ownership.
Prompt, Knowledge, \& RAG Engineering
- Develop and maintain prompt patterns, reusable instruction sets, source handling standards, and AI usage patterns for repeatable business use cases.
- Design AI solutions grounded in approved enterprise content and data, including SharePoint, Teams, Salesforce, SAP, Snowflake, Power BI, and governed repositories.
- Support RAG patterns, semantic search, metadata design, source attribution, content freshness, and conflict identification.
Enterprise Integration \& Data Alignment
- Partner with platform and data owners to ensure AI solutions use appropriate data sources, system\-of\-record definitions, data contracts, and integration patterns.
- Develop lightweight APIs, connectors, workflow integrations, and controlled data\-access patterns where needed to support AI solution delivery.
- Ensure AI designs respect operational system boundaries and enterprise reporting standards, especially around Salesforce, SAP, Snowflake, and Power BI.
Governance, Security, \& Compliance
- Apply responsible AI, privacy, security, access, auditability, source traceability, and human\-approval controls to AI and Claude solutions.
- Classify use cases by data sensitivity, business risk, required approvals, support model, and deployment readiness before production rollout.
- Maintain documentation for use cases, data sources, prompts, assumptions, controls, testing results, owners, risks, and success metrics.
Testing, Support, \& Adoption
- Create test plans for functional behavior, prompt quality, grounding, security, regression, user acceptance, and post\-deployment monitoring.
- Partner with QA and Training resources to produce test scripts, user guides, release notes, support procedures, demos, and adoption materials.
- Measure value through hours saved, cycle\-time reduction, improved quality, better customer support, user adoption, and decision\-support improvements.
Claude AI for Targeted Solutions
- Use Claude selectively for approved use cases involving long\-context reasoning, document analysis, comparison, summarization, structured drafting, and requirements synthesis.
- Apply Claude to policy/procedure review, executive communication, project documentation, training content refinement, technical explanation, and business analysis where appropriate.
- Support controlled Claude Code or comparable code\-assist scenarios for code review, test generation, refactoring, documentation, and migration planning under human\-review controls.
QUALIFICATIONS:
Experience
- 5–8 years in software engineering, automation, analytics engineering, AI solution delivery, enterprise application development, solution architecture, or a related technology role.
- Demonstrated, hands\-on Snowflake experience (including working with data models, schemas, and Cortex) required.
- Experience delivering workflow automation, integration, reporting, or AI\-enabled business solutions in a business\-facing environment.
- Experience with Microsoft 365, Power Platform, Copilot Studio, Azure AI, Azure OpenAI, Claude and comparable AI and automation tools.
- Experience integrating with enterprise platforms such as Salesforce, SAP, Snowflake, Power BI, SharePoint, Teams, or similar systems.
Technical Expertise
- Deep expertise in Snowflake (including Cortex) and Microsoft Copilot Studio.
- Solid proficiency in Python, JavaScript, SQL.
- Strong working knowledge of Power Automate, Azure AI, OpenAI, prompt engineering, AI agents, APIs, connectors, workflow orchestration, and RAG patterns.
- Practical understanding of Claude AI for document analysis, structured writing, long\-context summarization, requirements synthesis, technical explanation, and controlled code\-assist scenarios.
- Proficiency with SQL and practical experience with Python, scripting, APIs, low\-code/no\-code tools, or similar development methods.
- Understanding of data quality, metadata, lineage, access controls, retention, source attribution, and information governance.
Certifications
- Preferred: Microsoft Certified: Azure AI Engineer Associate, Microsoft Power Platform certification, Copilot Studio/Azure OpenAI/AI fundamentals certification, or Snowflake, Power BI, Salesforce, security, or related enterprise platform certification.
Soft Skills
- Strong communication skills with the ability to explain AI concepts, risks, limitations, and business value in practical terms.
- Hands\-on problem\-solving mindset with the ability to move from idea to prototype to controlled production deployment.
- Ability to challenge AI use cases when risks exceed value or when AI is not the right solution.
- Strong analytical thinking, attention to detail, documentation discipline, and cross\-functional collaboration.
What Success Looks Like
- Partner with internal and external engineering teams to prepare and leverage enterprise data in Snowflake, making it agent\-ready for agentic workflows.
- Build production\-ready AI agents on top of Snowflake, leveraging Snowflake Cortex as the AI layer and Microsoft Copilot Studio to design, deploy, and maintain agentic workflows that act on unified data from Salesforce, SAP, and our Fundraising App.
- Partner with internal and external engineering teams to prepare and leverage enterprise data in Snowflake, making it ready for agentic workflows.
- Develop forecasting and predictive capabilities in Snowflake, using Cortex to turn unified enterprise data into forward\-looking business insight.
- Develop deep mastery of our data models, schemas, and data — both inside and outside our environment — to ground AI solutions in accurate, well\-understood sources.
Additional Impact Areas
- Practical AI and Claude\-assisted use cases move from concept to controlled production with clear ownership, documented controls, and measurable value.
- AI outputs are grounded in approved enterprise data and content, cite sources where appropriate, identify conflicts, and avoid unsupported conclusions.
- Solutions integrate appropriately with Salesforce, SAP, Snowflake, Power BI, Microsoft 365, Power Platform, and approved AI tools without bypassing platform governance.
- Responsible AI, security, privacy, access, audit, and human\-approval requirements are embedded into solution design and release practices.
- Reusable prompt libraries, agent patterns, Claude usage patterns, testing methods, documentation standards, and governance checkpoints are adopted across departments.
REPORTING RELATIONSHIP: Chief Information Officer (CIO)
LOCATION:
This position is primarily remote. Occasional travel may be required for company meetings, training, team events, customer visits, or other business needs. Estimated travel: up to 10% of the time.
COMPENSATION \& BENEFITS:
- Base salary of $140,000\-$155,000 annually
- Annual Bonus: 5% of base salary, based on company and/or individual performance
- Medical insurance
- Dental insurance
- Vision insurance
- Wellness Program
- 401k Matching
- HSA/FSA
- LifeLock Identity Theft Protection
- STD/LTD
- Life Insurance
- Tuition Reimbursement
World’s Finest Chocolate is an equal opportunity employer and is committed to creating a diverse and inclusive workplace. We prohibit discrimination and harassment of any kind based on race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. We celebrate diversity and are dedicated to providing an environment of respect and inclusivity for all employees.
Salary Context
This $140K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At World's Finest Chocolate, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($147K) sits 31% below the category median. Disclosed range: $140K to $155K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
World's Finest Chocolate AI Hiring
World's Finest Chocolate has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $155K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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