Interested in this AI/ML Engineer role at Ramco Specialties Inc.?
Apply Now →Skills & Technologies
About This Role
Objective
The Business Intelligence \& AI Manager serves as a catalyst for business transformation by helping Ramco operate more efficiently, make better decisions, and scale effectively through process improvement, automation, and data\-driven insights.
This role works closely with business leaders to identify opportunities that improve productivity, eliminate inefficiencies, and deliver measurable business outcomes. The Manager leads the organization's business intelligence, automation, Power Platform, AI, and data initiatives while developing a team capabilities while fostering a culture of continuous improvement and operational excellence.
Success in this role is measured by business impact, including productivity gains, process improvements, enhanced decision\-making, adoption of automation and AI solutions, and the successful development of team capabilities.
Essential Functions
Business Transformation \& Process Improvement
Partner with business leaders to identify opportunities that improve productivity, eliminate inefficiencies, and drive measurable business outcomes.
Analyze business processes and recommend solutions that simplify operations, reduce manual effort, and improve organizational effectiveness.
Lead business process improvement initiatives from concept through implementation and user adoption.
Develop business cases, success metrics, and implementation plans for transformation initiatives.
Serve as a trusted advisor to stakeholders by translating business challenges into practical, scalable solutions.
Business Intelligence \& Decision Support
Partner with business leaders to define key performance indicators (KPIs), business metrics, and reporting requirements that support operational and strategic decision\-making.
Design, develop, and maintain dashboards, reports, semantic models, and analytical solutions that provide actionable business insights.
Transform complex data into meaningful information that enables faster and more informed decision\-making.
Promote data governance, data quality, and self\-service analytics capabilities throughout the organization.
Establish and maintain trusted sources of business information through effective data governance and data quality practices.
Automation, Power Platform \& AI Solutions
Identify, prioritize, design, and implement automation solutions that reduce manual effort, improve process consistency, and increase employee productivity.
Design, develop, and maintain business applications using Microsoft Power Apps to streamline workflows and improve operational efficiency.
Design, develop, and maintain automated workflows using Microsoft Power Automate and related Microsoft technologies.
Evaluate emerging AI technologies and identify practical opportunities to improve business operations and employee effectiveness.
Design, develop, and maintain AI\-powered solutions, including Copilot Studio agents and other intelligent agents, that improve productivity, knowledge access, customer service, and decision support.
Integrate AI and intelligent agent capabilities into business processes, applications, and reporting solutions to create measurable business value.
Data Platform \& Architecture
Design, develop, and maintain enterprise data warehouse solutions that support reporting, analytics, automation, and AI initiatives.
Create and manage dimensional, relational, and semantic data models that provide trusted and consistent business information.
Develop scalable data architectures that integrate data from ERP, CRM, EDI, manufacturing, quality, and other business systems.
Define and maintain enterprise business metrics, KPIs, hierarchies, and data definitions to ensure consistency across the organization.
Design and optimize ETL/ELT processes to improve data quality, availability, reliability, and performance.
Leadership \& Team Development
Provide day\-to\-day leadership, coaching, mentoring, and performance feedback to team members responsible for business systems and operational support activities.
Coordinate priorities and workloads to ensure alignment with business objectives and successful project delivery.
Foster a culture of collaboration, accountability, continuous improvement, and customer\-focused service.
Partner with internal stakeholders, vendors, consultants, and technology partners to successfully deliver business solutions and transformation initiatives.
Assist in developing departmental priorities, standards, and best practices for analytics, automation, AI, and business systems.
Continuous Improvement \& Innovation
Continuously evaluate new technologies, AI capabilities, and Microsoft platform enhancements for opportunities to improve business performance.
Establish and promote best practices for analytics, automation, AI, Power Platform, and business process improvement initiatives.
Measure and communicate the business impact of implemented solutions, including productivity improvements, cost savings, risk reduction, and operational efficiencies.
Stay current on industry trends and emerging technologies to ensure Ramco continues to leverage innovative solutions that support business growth and competitiveness.
REQUIRED QUALIFICATIONS
7\+ years of experience in business intelligence, business analysis, process improvement, automation, data analytics, or related disciplines.
3\+ years of experience leading, mentoring, or managing technical or business\-focused team members.
Demonstrated success partnering with business stakeholders to identify opportunities, define requirements, and deliver measurable business outcomes.
Demonstrated experience delivering measurable business outcomes through analytics, automation, AI, or process improvement initiatives.
Proven experience leading process improvement, operational efficiency, or business transformation initiatives.
Experience designing and implementing business solutions using Microsoft Power Platform, including Power Apps and Power Automate.
Experience developing Power BI solutions, semantic models, dashboards, and data\-driven decision support capabilities.
Experience designing, developing, and maintaining enterprise data warehouse and data integration solutions in Azure.
Strong understanding of data architecture, data governance, data quality, and analytics best practices.
Experience evaluating, implementing, or supporting AI\-enabled business solutions.
Strong communication, facilitation, project leadership, and stakeholder management skills.
Ability to translate business objectives into practical solutions that deliver measurable results.
Demonstrated experience coaching, mentoring, developing, and providing performance feedback to team members.
PREFERRED QUALIFICATIONS
Experience with Microsoft Fabric, Azure Data Factory, Azure Synapse, or similar cloud analytics platforms.
Experience developing and deploying Copilot Studio agents, AI agents, or other intelligent automation solutions.
Experience integrating AI capabilities into business processes, business applications, and decision\-support solutions.
Experience supporting Microsoft Dynamics NAV, Business Central, Dynamics 365 Sales, or similar ERP and CRM platforms.
Experience with EDI platforms and related business processes.
Experience in a manufacturing or distribution environment.
Bachelor's degree in Business, Information Systems, Computer Science, Data Analytics, or a related field preferred; equivalent experience and demonstrated success will be considered.
Personal Skills and Competencies
Business Acumen \- Understands business operations, financial drivers, customer needs, and organizational objectives. Evaluates opportunities and makes decisions based on business impact, value creation, and operational effectiveness.
Leadership \& Mentoring \- Provides coaching, guidance, and professional development to team members. Builds trust, accountability, and a culture focused on continuous improvement, collaboration, and results.
Strategic Thinking \- Recognizes opportunities to improve business performance and develops practical, scalable solutions aligned with organizational goals and priorities.
Business Process Improvement \- Continuously evaluates processes, identifies inefficiencies, and drives improvements that increase productivity, quality, consistency, and operational effectiveness.
Communication \& Influence \- Effectively communicates with all levels of the organization. Translates complex technical concepts into clear business recommendations and successfully influences stakeholders to achieve desired outcomes.
Analytical Thinking \- Uses data, facts, and business insight to identify root causes, evaluate alternatives, and develop effective solutions that support informed decision\-making.
Innovation \- Actively seeks opportunities to leverage analytics, automation, artificial intelligence, and emerging technologies to solve business challenges and create measurable value.
Customer Focus \- Develops strong relationships with internal stakeholders and consistently delivers solutions that address business needs, improve user experience, and support organizational success.
Accountability \- Takes ownership of commitments, drives initiatives to completion, and consistently delivers measurable results. Focuses on outcomes rather than activities.
Adaptability \- Thrives in a changing business and technology environment. Embraces continuous learning, innovation, and new ways of working.
Collaboration \- Works effectively across departments to build consensus, align priorities, and achieve shared business objectives.
Change Leadership \- Helps teams successfully adopt new processes, technologies, and ways of working. Demonstrates the ability to lead organizational change while maintaining engagement and focus on business outcomes.
Results Orientation \- Measures success through productivity improvements, operational efficiencies, employee adoption, enhanced decision\-making, and other tangible business outcomes
SUCCESS IN THE FIRST 12 MONTHS
Build strong relationships with business leaders across the organization and become a trusted advisor for process improvement, automation, AI adoption, and data\-driven decision\-making.
Develop a comprehensive understanding of Ramco's business processes, systems, key performance drivers, and strategic objectives.
Establish and communicate a roadmap for business intelligence, automation, AI, and process improvement initiatives aligned with organizational priorities.
Develop and implement a scalable semantic model and data warehouse foundation that improves data consistency, supports self\-service reporting, and enables future analytics and AI initiatives.
Deliver multiple high\-impact automation and Power Platform solutions that reduce manual effort, improve process consistency, and increase employee productivity.
Implement AI and Copilot\-based solutions, including intelligent agents, that enhance employee effectiveness, improve knowledge accessibility, and streamline business operations.
Improve visibility into business performance through meaningful KPIs, dashboards, data models, and analytical solutions that support faster and more informed decision\-making.
Establish governance, standards, and best practices for analytics, automation, Power Platform, AI, and data management initiatives.
Successfully mentor and develop team members while fostering a culture of continuous improvement, accountability, collaboration, and customer\-focused service.
Drive measurable business outcomes such as:
Reduced manual processing time
Increased employee productivity
Improved data quality and accessibility
Increased adoption of automation and AI solutions
Faster and more informed decision\-making
Improved process consistency and operational efficiency
Enhanced visibility into business performance
Be recognized by business leaders as a key contributor to operational excellence, business transformation, and organizational growth.
Physical Demands
While performing the duties of this job, the individual must be able to sit and talk or hear, walk, and climb stairs. Also, the employee must be able to stand, use hands to finger, handle or feel objects, tools, or controls; Reach with hands, arms, stoop, kneel, crouch, or crawl.
The partner must occasionally lift and/or move up to 15 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, and peripheral vision.
Work Environment
The work environment characteristics described here are representative of those that must be met by an employee while performing the essential duties of this position. Reasonable accommodations may be available to enable qualified individuals with disabilities to perform the essential functions.
The Business Intelligence \& AI Manager will frequently spend time on the manufacturing floor as well as in the office. While on the manufacturing floor, the employee must wear eye and hearing protection. Safety\-toed shoes are highly recommended while being around and on manufacturing equipment.
This job description is not a complete list of all responsibilities, duties or skills required for the job and is subject to review and change at any time, with or without notice, in accordance with the needs of Ramco Specialties, Inc. Since no job description can detail all the duties and responsibilities that may be required from time to time in the performance of a job, duties and responsibilities that may be inherent in a job, reasonably required for its performance, or required due to the changing nature of the job shall also be considered part of the job holder’s responsibility.
Salary Context
This $125K-$135K range is in the lower quartile 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 Ramco Specialties 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $125K to $135K.
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.
Ramco Specialties Inc. AI Hiring
Ramco Specialties Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hudson, OH, US. Compensation range: $135K - $135K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.