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Apply Now →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 a visionary and execution-oriented leader to serve as the Global Process Owner for Product Management, responsible for defining, designing, and institutionalizing Xylem's next-generation Product Management operating model. This role will lead a multi-year transformation to reimagine the end-to-end product management process, from idea generation through commercialization, lifecycle management, and retirement. Working across segments, divisions/business units, functions, and external partners, the Global Process Owner will architect a future-state framework that simplifies work, improves business outcomes, and embeds AI-enabled capabilities throughout the product lifecycle. Initially, this role will focus on designing and implementing the future-state process, tools, governance, and operating model. Following implementation, the role will assume long-term ownership of the process, driving adoption, optimization, performance management, and continuous innovation. This position offers a unique opportunity to shape how Product Management operates at Xylem for years to come.
CORE RESPONSIBILITIES:To perform the job successfully, an individual must be able to perform each essential duty satisfactorily.
Enterprise Product Management Transformation
- Lead the design and delivery of Xylem's future-state Product Management operating model.
- Reimagine end-to-end product management processes from concept through retirement, challenging legacy assumptions and identifying opportunities for simplification and acceleration.
- Develop a scalable framework that can be adopted consistently across segments, regions, and business units while accommodating necessary business differences.
- Define future-state workflows, decision points, governance structures, roles, responsibilities, and performance measures.
AI-Enabled Process Innovation
- Identify and integrate AI-enabled capabilities across the Product Management lifecycle.
- Partner with Digital, Data & Analytics, Product Management Excellence, and external experts to incorporate automation, predictive insights, and generative AI into core workflows.
- Evaluate emerging technologies and recommend investments that enhance decision quality, productivity, and business outcomes.
- Establish a roadmap for continuous advancement of AI-powered product management capabilities.
Cross-Functional Leadership
- Lead a diverse network of stakeholders spanning Product Management, Engineering, R&D, Commercial, Finance, Operations, IT, Data & Analytics, PLM, and executive leadership.
- Facilitate workshops, design sessions, and alignment activities to build consensus around future-state processes.
- Partner with external consultants, technology providers, and industry experts to leverage best practices and accelerate transformation.
- Drive enterprise-wide engagement and sponsorship to ensure successful adoption.
Process Governance & Ownership
- Establish global standards, policies, governance mechanisms, and decision frameworks supporting product management excellence.
- Define process performance metrics and business outcome measures.
- Ensure alignment between Product Management processes and enterprise initiatives, including PLM, ERP, digital transformation, innovation management, and portfolio governance.
- Serve as the long-term owner of the Product Management operating model after implementation.
Change Management & Capability Building
- Develop and execute enterprise change management strategies supporting process adoption.
- Partner with Product Management Academy and Community of Practice leaders to embed new ways of working.
- Create training, communication, and enablement approaches to scale adoption globally.
- Champion a culture of continuous improvement and innovation.
Continuous Improvement
- Monitor process effectiveness, user experience, compliance, and business impact.
- Establish mechanisms for ongoing feedback and optimization.
- Maintain a strategic roadmap for future enhancements and capability expansion.
- Benchmark against industry-leading organizations and emerging best practices.
Success Measures
Success in this role will be measured by:
- Successful design and implementation of the future-state Product Management operating model.
- Adoption of standardized processes across business units.
- Measurable productivity improvements enabled by AI capabilities.
- Simplification of product management workflows and reduction of administrative burden.
- Improved portfolio decision-making and execution effectiveness.
- Increased user satisfaction and process adoption.
- Establishment of sustainable governance and continuous improvement mechanisms.
- Demonstrated business impact from the transformation effort.
QUALIFICATIONS:
Required:
- Bachelor's degree in Business, Engineering, Technology, or a related field.
- 10+ years of experience in Product Management, Product Development, Process Excellence, Business Transformation, or related disciplines.
- Experience leading large-scale cross-functional transformation initiatives.
- Demonstrated ability to design and implement enterprise processes and operating models.
- Strong stakeholder management and executive influence skills.
- Experience driving organizational change across global organizations.
- Excellent facilitation, communication, and strategic thinking capabilities.
Preferred
- MBA or advanced degree.
- Experience with Product Lifecycle Management (PLM), Innovation Management, Portfolio Management, or Product Development processes.
- Experience incorporating AI, automation, digital capabilities, or workflow technologies into business processes.
- Experience working with consulting firms or leading enterprise transformation programs.
- Familiarity with process excellence methodologies such as Lean, Six Sigma, Business Process Management, or Design Thinking
The salary range for this position is $160,000 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 $160K-$175K range is below the median 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 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $160K to $175K.
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.
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, 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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