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About This Role
At Doosan Bobcat, our success is powered by our people. Through our winning culture and one global team working together, we deliver the best products and service to our customers – and make the world a better place. Join our team today and start building your career with a worldwide leader.
- Location: West Fargo-Main Office
Job Information
The AI Engineer Sr is an experienced technical leader within the AI Engineering team, responsible for designing, implementing, and optimizing enterprise-scale AI solutions that are production-ready and deliver measurable business impact. This role focuses on building advanced AI features — from generative AI to intelligent agents — embedded in products and platforms. While security, governance, and overarching AI architecture frameworks are managed by the AI Enablement team, the AI Engineer Sr partners closely with them to ensure solutions meet all compliance and architectural standards.
Role & Responsibility
Advanced AI Solution Development
- Lead end-to-end implementation of complex AI models and applications integrated into digital products and operational systems.
- Translate business requirements into scalable AI solutions that perform reliably in production environments.
Scalable Application & Agent Design
- Architect and develop high-performance AI applications and multi-step agents optimized for latency, accuracy, and maintainability.
- Extend and adapt agent frameworks to meet specialized use cases including predictive maintenance, diagnostics, and customer support automation.
- Align development decisions with AI Enablement team guidance on approved architectural and integration patterns.
Cross-functional Collaboration
- Work closely with product owner, software engineering, solution architect, and business teams to integrate AI features into production release cycles.
- Collaborate with AI Enablement on security reviews, architecture compliance, and governance approvals.
- Ensure ethical data usage and adherence to privacy and compliance requirements.
Innovation & Continuous Improvement
- Evaluate emerging AI tools, frameworks, and techniques for potential impact on product capabilities and operational efficiency.
- Lead initiatives that transform prototype concepts into production-grade capabilities with measurable ROI.
- Recommend optimizations to improve model inference speed, scalability, and cost efficiency.
Mentorship & Technical Leadership
- Provide technical guidance and mentorship to AI Engineer I and II team members.
- Lead code reviews, set engineering best practices, and foster a high-quality development culture.
- Managing 3rd party contractors and university projects.
Job Requirement
What Skills, Education, and Experience You’ll Need:
- Bachelor’s degree in Computer Science, AI, Data Science, or related field; Master’s preferred.
- 5–7 years in AI/ML engineering with a proven track record of delivering production-grade solutions.
- Expert-level proficiency in Python, major LLM frameworks (LangChain, Hugging Face, OpenAI APIs), and AI agent orchestration.
- Strong knowledge of cloud-native AI deployment (AWS) and containerization (Docker, Kubernetes).
- Experience building REST/GraphQL APIs and designing data pipelines.
- Effective communication skills to convey technical information to various stakeholders.
- Ability to work effectively in teams and lead cross-functional collaborations.
- Exposure to MLOps practices (CI/CD, model monitoring) and discussions on data privacy and ethics.
- Demonstrated ability to mentor and guide junior team members.
- Strong analytical skills to approach and solve complex challenges effectively.
- Open to adopting new technologies and enhancing workflows in a dynamic environment.
As a part of the Doosan Group, which employs more than 43,000 people in 38 countries worldwide, the company offers its customers products and solutions to help build stronger businesses and communities. Based in Seoul, South Korea, with its North America headquarters in West Fargo, North Dakota, Doosan Bobcat is a leading global manufacturer of construction, agriculture, landscaping and grounds maintenance equipment, attachments and services. The company is committed to empowering people to accomplish more.
Doosan is an Equal Opportunity Employer, including Individuals with Disabilities and Protected Veterans. All qualified applicants will receive consideration for employment without regard to sex, age, race, color, religion, creed, citizenship status, national origin, disability, marital status, sexual orientation, gender identity, protected veteran status, or any other status or characteristic protected by law. If you have a disability or special need that requires accommodation, please contact us at 701-476-4263.
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Bobcat, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Bobcat AI Hiring
Bobcat has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in West Fargo, ND, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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