Interested in this AI/ML Engineer role at Xylem?
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About This Role
Xylem is a Fortune 500 global water solutions company dedicated to advancing sustainable impact and empowering the people who make water work every day. As a leading water technology company with 23,000 employees operating in over 150 countries, Xylem is at the forefront of addressing the world's most critical water challenges. We invite passionate individuals to join our team, dedicated to exceeding customer expectations through innovative and sustainable solutions.
THE ROLE:
We are seeking an experienced, highly adaptable, curious, and fast\-thinking AI Solutions Architect to design, prototype, and deliver innovative AI capabilities across internal use cases. The ideal candidate combines strong foundational understanding of AI/ML technologies with a proactive drive to stay ahead of industry advancements, especially in generative AI and emerging architectures.
This role bridges business needs and technical execution, architecting dynamic solutions that leverage LLMs, traditional ML, data pipelines, RAG, agents, and enterprise integrations.
We offer a full benefits package to include Flexible Time Off (FTO), health, dental, vision, investment savings plan, bonus, equity incentive, and additional miscellaneous benefits.
CORE RESPONSIBILITIES:
Solution Architecture \& Innovation
- Translate business challenges into well\-scoped AI solutions, balancing feasibility, value, cost, and speed.
- Architect end\-to\-end AI systems, including data ingestion, model training, inference pipelines, monitoring, and governance.
- Design and refine LLM/RAG architectures, agent workflows, and prompt engineering patterns
- Rapidly explore emerging tools/techniques to extend AI capabilities across the organization
- Build reusable reference architectures and best practices for internal teams
Technical Leadership \& Execution
- Partner with engineering, data science, and product teams to guide implementation
- Conduct PoCs, prototypes, and pilots to validate technical suitability before scaling
- Ensure solutions meet performance, security, compliance, and cost\-efficiency requirements
- Integrate AI capabilities into existing systems, both cloud and legacy
- Work with MLOps/DevOps to establish robust CI/CD, observability, and lifecycle management
Strategy, Governance \& Cross\-Functional Collaboration
- Complement the Product team by defining the technical AI/ML roadmap, assessing feasibility, shaping the use\-case pipeline, and specifying the architecture required to deliver prioritized initiatives
- Provide expertise on responsible AI, privacy, and risk\-aware design
- Communicate complex concepts to stakeholders at all levels
- Mentor engineers and data scientists on architecture, quality, and emerging AI capabilities
QUALIFICATIONS:
Education
- Bachelor's or Master's degree in Computer Science, Engineering, or related field. OR equivalent work experience.
- Additional certifications in AI/ML technologies are preferred
Technical Background
- 7\+ years in solution architecture with proficiency in data architecture, including data pipelines, warehousing / Lakehouse concepts, APIs and integration patterns
- Strong understanding of security, privacy, compliance, and responsible AI principles, including access control, data protection, and risk mitigation
- Deep understanding of machine learning, generative AI, LLMs, RAG, prompt engineering, vector databases, and model evaluation frameworks
- Experience translating business requirements into solution architectures, technical roadmaps, and implementation plans
- Experience working cross\-functionally with engineering, product, data teams, and business stakeholders to deliver measurable outcomes
- Knowledge of MLOps/LLMOps practices such as CI/CD, model monitoring, observability, versioning, governance, and lifecycle management
Mindset \& Soft Skills
- Exceptionally curious, adaptive, and proactive, stays ahead of fast\-changing AI technologies
- Fast learner with ability to shift between conceptual and hands\-on tasks
- Strong problem solver with a “builder” mentality
- Comfortable with ambiguity, rapid experimentation, and iterative design
- Excellent communicator to both technical and business audiences
- Collaborative and supportive partner to cross\-functional teams
The estimated salary range for this position is $150,000 to $175,000 plus bonus. Starting pay is dependent on multiple factors, such as skills, experience, and work location, and is not typically at the top of the range. At Xylem, we offer a competitive compensation package with a generous benefit package, including Medical, Dental, and Vision plans, 401(k) with company contribution, paid time off, paid parental leave, and tuition reimbursement.
\#LI\-SUTTONMOORE
Join the global Xylem team to be a part of innovative technology solutions transforming water usage, conservation, and re\-use. Our products impact public utilities, industrial sectors, residential areas, and commercial buildings, with a commitment to providing smart metering, network technologies, and advanced analytics for water, electric, and gas utilities. Partner with us in creating a world where water challenges are met with ingenuity and dedication; where we recognize the power of inclusion and belonging in driving innovation and allowing us to compete more effectively around the world.
At Xylem, you'll not only contribute to solving water issues but also have the chance to make a difference through our paid Volunteer Program, Xylem Watermark. We prioritize our employees' well\-being through inclusion and belonging as well as our Employee Resource Groups (ERG). Proud to be an Equal Employment Opportunity (including disability and veterans) and Affirmative Action workplace, Xylem fosters an inclusive environment free from discrimination or harassment.
Please note that the information in this job description outlines the general nature of the position and is not an exhaustive list of duties. Xylem is dedicated to providing reasonable accommodations to enable all employees to perform their essential job functions. We reserve the right to modify this job description and assign additional duties as needed. Embrace the opportunity to be part of Xylem's transformative journey in shaping the future of water technology! \#XylemCareers \#GlobalImpact \#WaterInnovation
Salary Context
This $150K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Xylem, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($162K) sits 10% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Xylem AI Hiring
Xylem has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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