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About This Role
AI Solutions & Automation Engineer
Location: 1 Dixon Square, Chestertown, MD 21620
Work Hours: 8:00 AM - 5:00 PM, Monday through Friday
Pay Range: $60,000 - $100,000/Annually
We do not provide sponsorships to work in the United States.
Make the Right Connection—Build Your Career with Dixon!
The Dixon Group is seeking an AI Solutions & Automation Engineer to join our team in Chestertown, MD! In this role, you will lead the end-to-end lifecycle of our enterprise AI platform, automated workflows, and agentic solutions. This cross-functional position bridges software engineering, data science, and business process design. You will partner with leaders throughout the company to identify operational challenges and design, build, deploy, and support secure, scalable, and autonomous AI-powered agents and multi-agent systems. Your work will transform complex business requirements into measurable productivity gains while ensuring rigorous data governance, safety, and operational reliability.
At The Dixon Group, we value the contributions of our Military Veterans and proudly employ our nation’s heroes. Veterans are strongly encouraged to apply.
What You'll Do:
- Partner directly with business units and corporate leaders across operations, sales, customer service, finance, IT, engineering, and manufacturing to identify high-value AI opportunities and drive AI-driven transformation.
- Conduct thorough process analysis, workflow assessments, and operational audits to gauge existing business cases and determine where AI agents or intelligent automation can yield productivity gains.
- Translate operational needs into detailed technical solution designs, prioritizing projects based on business value, technical feasibility, and implementation complexity.
- Design, build, deploy, and maintain robust, autonomous, and semi-autonomous AI-powered single-agent and multi-agent systems that execute complex workflows and automate manual processes.
- Develop and optimize custom Copilot Studio agents, AI-powered workflows, and intelligent integrations leveraging modern LLM frameworks, RAG (Retrieval-Augmented Generation) architectures, and vector databases. Integrate agents with ERP, CRM, SQL databases, SharePoint, Teams, manufacturing systems, and third-party SaaS platforms.
- Own and continuously evolve the Dixon Enterprise AI Platform reference architecture, managing model serving, orchestration engines, data grounding pipelines, and observability infrastructure. Architect platform service catalogs (LLM gateway/routing, prompt lifecycle management, vector stores) to reduce time-to-production.
- Establish and enforce company-wide AI governance standards, model risk management programs, data protection policies, and access controls. Implement permission scoping, PII/data privacy controls, and audit trails in collaboration with IT and cybersecurity teams.
- Create training programs, user documentation, and demonstrations to ensure safe, effective adoption of approved AI tools. Train, mentor, and support team members on advanced agentic techniques and prompt optimization.
- Measure and report on AI program ROI, employee adoption rates, business impact, and operational performance improvements.
What We're Looking For:
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Business, or a related field (or equivalent combination of education and experience).
- 3-5+ years of software development and technology solutions implementation experience in a business environment.
- Proven experience successfully designing, building, deploying, and maintaining production-ready AI solutions, AI agents, or intelligent workflow automations.
- Deep technical familiarity with agentic AI architectures, multi-agent orchestrations, and data compliance frameworks.
- Strong technical skills in Microsoft Copilot Studio, Power Automate, Power Apps, Power Platform, Microsoft 365, Microsoft Graph, Dataverse, Fabric, and Azure Services (Azure AI Services, Azure OpenAI).
- Solid experience with databases and vector technologies including SQL, MS SQL Vector data type, vector stores, and RAG pipelines.
- Experience with programming languages and tools including Python, REST APIs, JSON, Git source control, Visual Studio, and MCP Servers.
- Excellent communication and project management skills with the ability to translate business needs into practical technical applications.
- Strong analytical and problem-solving skills with a commitment to balancing innovation with operational reliability and security.
- Excellent verbal and written communications skills.
- Certifications preferred, but not required: AB-620 Microsoft Certified: AI Agent Builder Associate, PL-400 Microsoft Certified: Power Platform Developer Associate, or related certifications in AI, cloud technologies, cybersecurity, or project management.
What We Offer:
- Medical, dental, and vision insurance for you and your family
- Competitive salary
- Bonus programs
- 401K retirement plan
- Training opportunities
- Tuition reimbursement
- Paid vacation, PTO, and holidays
- Gym reimbursements, and more!
Who We Are: The Dixon Group is a global, family-owned manufacturing company with a history of over 100 years of operation in the U.S.A. Based in Chestertown, Maryland, with distribution centers located worldwide. The company has a diverse workforce and a strong values-based culture. As an innovator in the hose coupling industry, our mission is to provide real solutions for our customers while fostering a supportive and collaborative work environment.
Join us and be a part of a team that values your contributions and supports your goals!
We do not provide sponsorships to work in the United States.
The Dixon Group is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices and laws. Accordingly, to the fullest extent required by applicable law, The Dixon Group strictly prohibits and does not tolerate discrimination against employees, applicants, or any other covered persons.
The Dixon Group is committed to protecting the privacy rights of its employees and job applicants to the fullest extent required by applicable law. To that end, personal information will be collected solely for those legitimate business purposes recognized by law, and then maintained in a manner consistent with all applicable laws and regulations pertaining to document retention requirements. The Dixon Group does not sell personal information to third parties, and does not share such personal information with third parties except when authorized by law to do so (e.g., in response to a lawful subpoena; mandatory tax reporting; etc.)
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $60K-$100K 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 Dixon Group Services, 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 ($80K) sits 62% below the category median. Disclosed range: $60K to $100K.
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.
Dixon Group Services AI Hiring
Dixon Group Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chestertown, MD, US. Compensation range: $100K - $100K.
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
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