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About This Role
Position Purpose:
The Artificial Intelligence (AI) Developer designs, develops, and implements scalable AI and machine learning (ML) solutions that improve business operations, decision-making, and digital capabilities. Working closely with Information Technology (IT), data, and business stakeholders, this role translates business needs into production-ready solutions while ensuring alignment with enterprise architecture, data governance, cybersecurity standards, and continuous innovation.
Essential Functions:
- Design, build, fine‑tune, test, deploy, and maintain scalable AI, ML models, and solutions using modern AI services, algorithms, neural networks, GenAI, Agentic AI, and deep learning architectures.
- Conduct research, experimentation, and testing to develop predictive models and innovative AI solutions that enhance business capabilities and operational efficiency.
- Develop and manage data pipelines, datasets, and model training workflows, ensuring high‑quality data for training, validation, and evaluation.
- Leverage big data computation and storage tools to create prototypes and handle large volumes of structured and unstructured data efficiently.
- Integrate AI solutions into existing applications, web services, and business workflows, ensuring smooth deployment, scalability, and performance, particularly within Azure cloud environments.
- Collaborate closely with cross‑functional teams, including IT, data, and business stakeholders, to identify needs, access and prepare datasets, and translate requirements into production‑ready AI solutions.
- Continuously monitor, evaluate, and optimize model and system performance to achieve optimal accuracy, reliability, and efficiency.
- Ensure AI systems align with enterprise architecture standards, data governance policies, cybersecurity requirements, and responsible AI practices.
- Provide technical support for business‑critical situations, assist with reporting and analysis, and troubleshoot issues related to AI systems and applications.
- Document AI models, systems, development processes, and workflows, and prepare technical documentation or training materials as needed.
- Stay current with advancements in AI, ML, and cloud technologies, applying new techniques and best practices to drive continuous improvement.
- Perform other related duties as assigned.
Education and Training
- A bachelor's degree in a computer science, IT, or related field is required.
- A master's degree in computer science, IT, or a related field is a plus.
- Education, training, or hands-on experience in ML, deep learning, neural networks, and predictive modeling is required.
- Strong training or experience in programming languages is essential.
- Training or education with cloud platforms, preferably Microsoft Azure, is necessary.
- Related professional certifications such as, but not limited to Azure AI Engineer, Azure Data Scientist, or other AI/ML certifications are a plus.
Minimum Qualifications
- A minimum of 7 years' experience in a software-related field.
- A minimum of 2 year's experience in an AI development related role.
- Demonstrated experience with artificial intelligence and machine learning concepts, including development training, evaluation, and deployment.
- Experience with statistics, predictive analytics, data science, and reporting, including applying analytical techniques to real‑world business problems.
- Proficiency in programming and scripting, including experience with languages commonly used in AI and software development such as, but not limited to, Python.
- Experience with data collection, cleaning, preprocessing, manipulation, and pipeline management for structured and unstructured datasets.
- Familiarity with web development concepts, Application Programming Interfaces (APIs), or system integrations supporting AI or data‑driven applications.
- Foundational experience with cloud platforms, especially Microsoft Azure, and familiarity with deploying or supporting applications or services.
- Strong analytical, mathematical, and problem‑solving skills, with the ability to troubleshoot complex technical issues.
- Understanding of the software development lifecycle (SDLC), version control, testing, and documentation practices.
- Ability to work independently, manage multiple priorities, and meet deadlines in a fast‑paced environment.
- Strong verbal, written, and interpersonal communication skills, with the ability to work effectively with cross‑functional teams and stakeholders at all organizational levels.
- Ability to communicate project status clearly, identify risks or issues, and escalate concerns appropriately.
- Prior experience in a manufacturing environment, particularly within the commercial automotive industry, is desirable but not required.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or other characteristics protected by law.
Req Benefits: We offer a full complement of benefits that include medical, dental, and vision insurance; life, travel, and disability insurance; a flexible spending account; 401K plans; a pension plan; paid holidays and vacation. We also offer financial assistance programs if you decide to adopt a child or want to pursue further education. We have a comprehensive wellness program and an Employee Assistance Program, that supports individuals in the proactive management of their overall health and wellness.
Compensation: In accordance with applicable pay transparency laws, the salary range for this position is $90,000.00-100,000.00 annually. Final compensation will be determined based on factors such as experience, education, qualifications, and other job-related criteria
Salary Context
This $90K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Hendrickson, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $90K to $100K.
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.
Hendrickson AI Hiring
Hendrickson has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Woodridge, IL, US. Compensation range: $100K - $100K.
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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