Interested in this AI/ML Engineer role at Watts Water Technologies?
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About This Role
We’re Watts. Together, we’re reimagining the future of water.
We feel proud every day about what we do. We're all part of the same crucial mission, no matter what function we support - it's to provide safe, clean water for the world, and to protect our planet's most valuable resource.
What we do:
For 150 years, Watts has built best-in-class products that are trusted by customers in residential and commercial settings across the world. We are at the forefront of innovation, working with cutting-edge technology to provide smart and connected, sustainable water solutions for the future. Watts is a leading brand with a quality reputation — and we have a dynamic future ahead.
Scope of Position
Reporting to the VP, Intelligent Process Automation, the Generative AI Product Owner is responsible for defining, delivering, and continuously improving enterprise-grade Generative AI capabilities, with an initial and primary focus on Microsoft Copilot across M365, Power Platform, and integrated business applications with a consideration for SAP Joule, BTP, and S/4 HANA.
This role sits at the intersection of business value, user experience, and technology execution. The Product Owner will translate business needs into a clear GenAI product roadmap, prioritize high-value use cases, and partner closely with IT, Security, Data, Legal, and Business leaders to ensure Copilot and Joule are deployed responsibly, securely, and at scale.
The role emphasizes value realization, adoption, and governance, not experimentation for its own sake.
This position is hybrid and based in Andover, MA.
Primary Job Duties and Responsibilities
- Own the Generative AI product vision and roadmap, with primary focus on Microsoft Copilot across M365 and Power Platform along with developments in SAP Joule
- Define and track measurable outcomes tied to productivity, cost reduction, and business enablement.
- Identify and prioritize high-value GenAI use cases across enterprise functions.
- Lead enterprise rollout and adoption of AI Solutions, ensuring they are embedded into real business workflows.
- Conduct training and workshops on copilot and other power applications to drive personal productivity. Educate end users on compliance and governance.
- Own and prioritize the GenAI product backlog, translating business needs into clear, secure user stories.
- Partner with engineering and platform teams to deliver GenAI capabilities using agile, incremental delivery.
- Ensure all GenAI solutions comply with AI governance, security, privacy, and Responsible AI standards.
- Act as the primary GenAI product interface between IT, business leaders, and executive stakeholders.
Required Qualifications
- 5+ years of experience as a Product Owner, Product Manager, or similar role in IT or digital platforms.
- Hands-on experience delivering Microsoft and SAP ecosystem solutions (M365, Power Platform, Azure, Joule, BTP, S/4 HANA).
- Practical exposure to Generative AI tools, preferably Microsoft Copilot or similar enterprise AI platforms.
- Experience working in regulated or enterprise environments with strong security and governance requirements.
- Strong understanding of:
+ Agile product delivery and backlog management
+ Enterprise productivity tools and workflows
+ Data privacy, security, and AI risk considerations
- Ability to translate business problems into product features and measurable outcomes.
Preferred Qualifications
- Excellent communication and stakeholder management skills.
- Experience with Microsoft Copilot Studio, Power Automate, Power Apps, or Azure OpenAI or SAP Joule, BTP, ABAP.
- Familiarity with Responsible AI frameworks and AI governance models.
- Experience driving technology adoption and change at scale.
- Background in manufacturing, industrial, or B2B environments.
General Applicable Company Competencies
- Commitment to Watts’ values of integrity, accountability, continuous improvement and innovation, and transparency.
- Punctuality and dependability.
- Ability to be flexible and adapt to changing work priorities and stressful conditions.
- Adherence to all personnel policies, procedures, and standards of process as implemented by Watts.
- Maintain productive and collaborative relationships with other Watts employees.
- Adherence to Watts’ seven cultural beliefs: Growth Mindset, Customer-Focused Innovation, Constant Communication, Clear Goals, Collaborate Globally, Be Inclusive, and Take Action.
Working Conditions:
While performing the job duties, you will be working in an office environment. You will be required to work in the office at the Andover, MA location three days per week (Monday – Wednesday) and can work remotely two days per week (Thursday and Friday).
Physical Requirements: Specific physical abilities required for this position include, but are not limited to:
- Ability to remain seated at a desk or workstation for extended periods.
- Ability to perform repetitive tasks like typing on a keyboard or using a mouse for extended periods.
- Ability to physically move around the office, organize or transport files, packages, or other office-related materials.
- Ability to read documents, use a computer, and perform data entry tasks.
- Ability to communicate clearly with management and coworkers, particularly in meetings or phone calls.
- Ability to operate standard office equipment such as computers, printers, phones, and copiers.
- Ability to occasionally lift and carry light objects, such as office supplies, documents, or small equipment.
Pay Range:
The expected salary range for this position is $132,000- $153,000 yearly. Actual compensation will be dependent upon individual skills, experience, qualifications, and applicable law.
Nothing in this job description restricts Watts’ right to assign or reassign duties, responsibilities, and working hours/conditions to this position at any time. This position is “at will,” which means that either the employee or Watts may terminate the employment relationship at any time, with or without notice, and for any lawful reason.
Watts in it for you:
*Please note that the following**benefits apply only to permanent roles and do not apply to internship roles.*
- Competitive compensation based on your skills, qualifications and experience
- Comprehensive medical and dental coverage, retirement benefits
- Family building benefits, including paid maternity/paternity leave
- 10 paid holidays and Paid Time Off
- Continued professional development opportunities and educational reimbursement
- Additional perks such as fitness reimbursements and employee discount programs
- Learn more about our benefit offerings here: https://tapintowattsbenefits.com/
How we work:
At Watts, our culture is team-oriented and supportive. Employees here genuinely care about the quality of their work, and about each other. Our people are the heart of who we are and contribute to our longevity and continued success.
And this is a place where you can have a big career. No matter your role, there are opportunities for learning and development, and your daily contributions make a meaningful impact on the lives of people who use our products and on the future of water.
Watts is committed to equal employment opportunity. We follow a policy of administering all employment decisions and personnel actions without regard to race, color, religion, creed, sex, pregnancy, national origin, sexual orientation, age, physical or mental disability, genetic disposition or carrier status, marital status, military or veteran status, minorities, or any other category protected under applicable federal, state, or local law. Consistent with the obligations of state and federal law, Watts will make reasonable accommodations for qualified individuals with disabilities. Any employee who needs a reasonable accommodation should contact Human Resources.
Salary Context
This $132K-$153K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Watts Water Technologies, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($142K) sits 7% below the category median. Disclosed range: $132K to $153K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Watts Water Technologies AI Hiring
Watts Water Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Andover, MA, US. Compensation range: $153K - $153K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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