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About This Role
### Job Title
AI Solutions Engineer
### Job Description
Join our team to design, build, and scale enterprise AI solutions that transform business processes and drive innovation. This role combines Generative AI, Microsoft Power Platform, Databricks, and modern software engineering to deliver intelligent applications, automation, and data\-driven solutions across Philips.
Your role:
- Design, develop, and deploy enterprise AI solutions from concept through production, leveraging large language models (LLMs), AI agents, prompt engineering, and Retrieval\-Augmented Generation (RAG). Own solutions throughout their lifecycle, including testing, deployment, monitoring, and continuous improvement.
- Build AI\-powered business applications using Microsoft Power Platform, including Power Apps, Power Automate, Power BI, and Copilot Studio. Create intelligent workflows that connect enterprise systems, APIs, Microsoft 365, and third\-party platforms to streamline operations and improve user experiences.
- Develop scalable data and AI solutions using Databricks, SQL, and Python to support analytics, automation, and business transformation initiatives. Partner closely with Data Operations and business teams to ensure solutions are reliable, secure, and aligned with organizational goals.
- Collaborate with business leaders to identify opportunities where AI and automation can improve efficiency, decision\-making, and business outcomes. Prototype emerging AI capabilities and evaluate new models, frameworks, and technologies to address complex business challenges.
- Utilize AI\-assisted development tools such as GitHub Copilot, OpenAI Codex, and Claude Code to accelerate software development while maintaining high standards for quality, security, and maintainability. Contribute to reusable AI components, engineering standards, and best practices that enable broader AI adoption across the organization.
You're the right fit if:
- You've acquired 3\+ years of experience developing enterprise software, AI, automation, or intelligent business solutions.
- Your experience includes developing applications using Microsoft Power Platform (Power Apps, Power Automate, Power BI, and/or Copilot Studio), using Databricks, SQL, and Python to develop data solutions that support AI and business applications, building AI\-enabled applications using large language models (LLMs), AI agents, prompt engineering, or related technologies, and integrating enterprise applications using REST APIs and modern integration technologies.
- You have worked with business stakeholders to translate business needs into technology solutions and delivering solutions that support business and operational objectives.
- You have a Bachelor's degree in Computer Science, Information Systems, Software Engineering, Artificial Intelligence, Data Science, or a related technical field.
- You're a strong problem\-solver with the ability to learn and apply emerging technologies in a rapidly evolving environment.
- You must be able to successfully perform the following minimum Physical, Cognitive and Environmental job requirements with or without accommodation for this position .
How we work together
We believe that we are better together than apart. For our office\-based teams, this means working in\-person at least 3 days per week. Onsite roles require full\-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
This is an office role.
About Philips
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help improve the lives of others.
- Learn more about our business .
- Discover our rich and exciting history.
- Learn more about our purpose.
- Learn more about our culture.
Philips Transparency Details
The pay range for this position in TN is $96,000 to $152,000\.
The pay range for this position in MA is $107,000 to $170,000\.
The actual base pay offered may vary within the posted ranges depending on multiple factors including job\-related knowledge/skills, experience, business needs, geographical location, and internal equity.
In addition, other compensation, such as an annual incentive bonus, sales commission or long\-term incentives may be offered. Employees are eligible to participate in our comprehensive Philips Total Rewards benefits program, which includes a generous PTO, 401k (up to 7% match), HSA (with company contribution), stock purchase plan, education reimbursement and much more. Details about our benefits can be found here .
At Philips, it is not typical for an individual to be hired at or near the top end of the range for their role and compensation decisions are dependent upon the facts and circumstances of each case.
Additional Information
US work authorization is a precondition of employment. The company will not consider candidates who require sponsorship for a work\-authorized visa, now or in the future.
Company relocation benefits will not be provided for this position. For this position, you must reside in or within commuting distance to Nashville, TN (preferred) or Cambridge, MA .
This requisition is expected to stay active for 45 days but may close earlier if a successful candidate is selected or business necessity dictates. Interested candidates are encouraged to apply as soon as possible to ensure consideration.
*Philips is an Equal Employment and Opportunity Employer including Disability/Vets and maintains a drug\-free workplace.*
Salary Context
This $96K-$170K 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 Philips, 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 ($133K) sits 39% below the category median. Disclosed range: $96K to $170K.
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.
Philips AI Hiring
Philips has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US. Compensation range: $170K - $254K.
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
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