Interested in this AI/ML Engineer role at Dacut?
Apply Now →Skills & Technologies
About This Role
Position Description:
About the Role
DACUT Cannabis is seeking an experienced AI Data & Analytics Engineer to build intelligent reporting, automation, and forecasting solutions across our vertically integrated cannabis organization.
This position will combine artificial intelligence, financial analytics, SQL, Power BI, and data engineering to improve decision-making across retail, cultivation, processing, distribution, and corporate operations. The AI Data & Analytics Engineer will transform complex financial and operational data into scalable dashboards, predictive models, automated workflows, and actionable business insights.
The ideal candidate has strong technical experience with AI-powered analytics, financial modeling, SQL development, and Power BI. Experience supporting a vertically integrated, multi-location organization is highly preferred. Essential Responsibilities Artificial Intelligence & Automation* Develop and implement AI-powered tools that improve forecasting, reporting, operational efficiency, and financial decision-making.
- Build machine-learning models for revenue forecasting, demand planning, inventory optimization, labor analysis, and expense management.
- Identify opportunities to automate recurring financial, operational, and executive reporting.
- Develop AI-assisted workflows using large language models, APIs, and automation platforms.
- Create systems that identify trends, anomalies, financial risks, and operational inefficiencies.
- Evaluate emerging AI technologies and recommend practical applications across the organization.
- Establish safeguards and validation processes to ensure AI-generated insights are accurate, secure, and reliable.
Financial Analytics* Analyze revenue, gross margin, operating expenses, labor costs, inventory value, profitability, and cash-flow trends.
- Build financial models, forecasts, budgets, and scenario analyses for executive leadership.
- Develop location-level, department-level, product-level, and brand-level profitability reporting.
- Analyze performance across retail, cultivation, processing, distribution, and corporate operations.
- Partner with Finance and Accounting to validate financial data and maintain consistent reporting standards.
- Create automated reporting for actual performance compared with budgets, forecasts, and prior periods.
- Support strategic planning by identifying cost-saving opportunities, revenue trends, and performance risks.
Power BI & Business Intelligence* Design, develop, and maintain executive-level Power BI dashboards and reporting tools.
- Build scalable data models using Power Query, DAX, calculated measures, and relationships.
- Create dashboards for financial performance, retail sales, inventory, cultivation yields, production output, labor, and operational KPIs.
- Implement row-level security, scheduled refreshes, and data-quality controls.
- Improve dashboard usability by presenting complex information in a clear and actionable format.
- Establish consistent definitions for KPIs across departments and business entities.
SQL & Data Engineering* Write, optimize, and maintain complex SQL queries, stored procedures, views, and data transformations.
- Build and manage ETL/ELT pipelines that consolidate information from multiple systems.
- Integrate data from POS, ERP, accounting, payroll, inventory, compliance, CRM, and operational platforms.
- Create centralized datasets that support reporting, forecasting, and AI applications.
- Monitor data pipelines, troubleshoot failures, and resolve inconsistencies between source systems.
- Develop processes for data cleansing, validation, reconciliation, and documentation.
- Support the development of a reliable data warehouse or centralized reporting environment.
Vertically Integrated Operations* Connect data across retail, cultivation, processing, distribution, inventory, finance, and corporate functions.
- Develop reporting that tracks products and costs throughout the entire operational lifecycle.
- Analyze cultivation yields, production costs, inventory movement, retail sell-through, product margins, and overall profitability.
- Identify gaps between departments that affect data accuracy, inventory control, or financial performance.
- Partner with leaders across each division to understand operational challenges and develop data-driven solutions.
- Support standardized reporting across multiple locations, departments, brands, and legal entities.
Data Governance & Compliance* Maintain accurate, consistent, and secure financial and operational data.
- Develop documentation for data sources, models, calculations, dashboards, and automated processes.
- Establish validation controls to identify missing, duplicated, or inaccurate information.
- Protect confidential financial, employee, customer, and operational information.
- Support reporting requirements within a highly regulated industry.
- Ensure AI tools and data integrations follow company security, privacy, and compliance standards.
Required Qualifications* Bachelor’s degree in Data Science, Computer Science, Data Analytics, Finance, Accounting, Engineering, or a related field.
- At least 3–5 years of experience in data analytics, business intelligence, data engineering, financial analytics, or a related position.
- Advanced SQL skills, including complex joins, CTEs, data transformations, query optimization, and database troubleshooting.
- Advanced experience developing Power BI dashboards, semantic models, Power Query transformations, and DAX calculations.
- Demonstrated experience applying AI, machine learning, or predictive analytics to real business problems.
- Strong understanding of financial statements, budgeting, forecasting, profitability, and operational finance.
- Experience integrating and reconciling data from multiple business systems.
- Ability to translate complex technical findings into clear recommendations for executives and operational leaders.
- Strong analytical, organizational, and problem-solving skills.
- Ability to work onsite at the West Bloomfield corporate office.
Preferred Qualifications* Experience supporting a vertically integrated or multi-entity organization.
- Experience within cannabis, manufacturing, retail, distribution, consumer packaged goods, or another highly regulated industry.
- Experience with Python, Microsoft Fabric, Azure, Databricks, APIs, or cloud-based data platforms.
- Experience building machine-learning models, AI agents, or large language model integrations.
- Knowledge of ERP, POS, inventory-management, payroll, and accounting systems.
- Experience analyzing product costing, inventory valuation, supply-chain performance, and multi-location profitability.
- Familiarity with cannabis platforms such as METRC, Dutchie, Alpine IQ, or similar systems.
- Experience developing enterprise data governance and KPI standardization.
Core Competencies* AI and machine-learning implementation
- Financial modeling and forecasting
- Power BI dashboard development
- Advanced SQL development
- Data engineering and system integration
- Business intelligence and executive reporting
- Process automation
- Data governance and quality control
- Vertically integrated operational analysis
- Cross-functional communication
What Success Looks Like* Leadership has timely access to accurate financial and operational insights.
- Manual reporting processes are replaced with reliable automated workflows.
- Financial forecasts become more accurate and actionable.
- Data from retail, cultivation, processing, inventory, and corporate systems is connected.
- Power BI dashboards provide a consistent view of performance across the organization.
- AI solutions produce measurable improvements in efficiency, profitability, and decision-making.
#Priority26
Salary Context
This $90K-$110K 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 Dacut, 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 ($100K) sits 53% below the category median. Disclosed range: $90K to $110K.
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.
Dacut AI Hiring
Dacut has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Bloomfield Township, MI, US. Compensation range: $110K - $110K.
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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.