Interested in this AI/ML Engineer role at WesTech Engineering, LLC?
Apply Now →Skills & Technologies
About This Role
Our mission at WesTech is to help communities thrive by delivering high-quality water solutions with superior service – safely and sustainably. To achieve this mission, we recruit great people to be part of our collegial and collaborative teams in engineering, design, project management, field service, plant operations, professional services and more.
As one of the leading operating brands of Swire Water, our values of integrity, humility, endeavor, excellence and continuity make this an outstanding working environment. Our continuing success creates attractive opportunities for purpose-driven professionals to develop new skills, support vital industries, and grow their careers.
Summary
WesTech is seeking an AI Manager - Adoption & Enablement. This ONSITE role is responsible for accelerating the practical adoption of Artificial Intelligence across WesTech by helping the business identify, develop, scale, and govern high-value AI use cases.
This ONSITE role serves as the operational leader of the company’s AI program, working with AI Champions, Business Analysts, Digital teams, business leaders, and external partners to turn ideas into measurable business outcomes. The role is not primarily a developer. Its purpose is to build organizational capability, remove barriers, coordinate execution, and ensure AI initiatives align with business priorities, governance requirements, and WesTech’s long-term AI vision.
Candidates for this position may be required to pass pre-employment and periodic screenings, including background checks, drug and alcohol tests, or other necessary assessments.
Some of the work you will do:
AI Strategy Execution
- + Execute the enterprise AI roadmap and translate strategic objectives into practical initiatives. + Identify opportunities where AI can improve productivity, quality, decision-making, customer experience, or business processes. + Help business leaders prioritize AI investments based on value, feasibility, risk, and organizational readiness. + Track adoption, benefits realization, and overall program progress.
AI Champions Program Leadership
- + Lead and support the company’s AI Champions network. + Coach champions on identifying, documenting, and advancing use cases. + Facilitate collaboration between champions to avoid duplication and promote reuse of successful solutions. + Organize workshops, working sessions, demonstrations, and knowledge-sharing activities. + Help champions move ideas from experimentation into repeatable business processes.
Use Case Development and Pipeline Management
- + Establish and maintain a visible pipeline of AI use cases across the company. + Work with analysts and business stakeholders to define problems, expected outcomes, business value, data needs, and implementation requirements. + Distinguish between opportunities appropriate for individual productivity, departmental solutions, and enterprise-scale implementation. + Monitor progress, remove execution barriers, and connect use cases with the resources required to move forward. + Promote successful use cases for reuse and broader organizational adoption.
Business Partnership and Change Leadership
- + Partner with leaders and teams across Sales, Products, Solutions, Supply Chain, Engineering, Manufacturing, and Corporate Services. + Serve as a practical advisor on how AI can support business priorities and improve work. + Facilitate discovery sessions and workshops to identify and shape AI opportunities. + Support change management, communication, training, and user adoption. + Capture and share success stories, lessons learned, and reusable approaches.
Vendor and Partner Management
- + Research relevant AI technologies, platforms, vendors, and industry developments. + Identify consulting, technology, implementation, and solution partners that can accelerate WesTech’s AI vision. + Evaluate partner capabilities, fit, cost, risk, and ability to deliver sustainable business value. + Coordinate proofs of concept, pilot efforts, and partner-supported implementations. + Provide recommendations on when to build internally, use existing platforms, or engage external partners.
Governance and Responsible AI
- + Support the implementation and ongoing improvement of AI governance, policies, standards, and lifecycle controls. + Ensure initiatives align with security, privacy, compliance, data governance, architecture, and records-management requirements. + Guide teams toward approved tools and appropriate data use. + Coordinate required reviews and escalations for higher-risk or enterprise-impacting use cases. + Maintain visibility into AI solutions that become operationally important to the business.
Capability Building
- + Develop AI learning paths, playbooks, examples, and enablement programs. + Increase AI literacy and practical capability across all levels of the organization. + Help employees understand how to use AI effectively within their work and responsibilities. + Build a culture of responsible experimentation, shared learning, and continuous improvement. + Focus adoption on real business outcomes rather than technology for its own sake.
Here are the skills that you need:
- Bachelor’s degree in Information Technology, Business, Engineering, Data Analytics, or a related field, or equivalent relevant experience.
- Five or more years of experience leading technology, digital transformation, process improvement, innovation, analytics, automation, or AI-related initiatives.
- Experience coordinating cross-functional programs involving business and technical stakeholders.
- Strong facilitation, communication, relationship-management, and change-leadership skills.
- Demonstrated ability to move ambiguous ideas toward defined outcomes and execution.
- Working understanding of generative AI, automation, data-driven solutions, and the risks associated with enterprise AI adoption.
Here are the skills and qualifications that will set you apart:
- Experience with Microsoft Copilot, Copilot Studio, Power Platform, Azure AI, or comparable enterprise AI platforms.
- Experience evaluating and managing consulting firms, technology vendors, and implementation partners.
- Experience in industrial manufacturing, engineering, EPC, water treatment, or a related environment.
- Experience developing business cases and measuring value realization from technology investments.
- Experience with MCP, Python, PySpark to connect agents to our data sources.
Physical Requirements:
- Ability to perform work primarily at a computer workstation and use a phone for extended periods.
- Frequent talking, hearing, visual focus, and participation in virtual and in-person meetings, training, and presentations.
- Regular movement throughout the facility, with reasonable accommodations available for individuals with disabilities.
- Occasional travel may be required to support business locations, training, or program activities.
Benefits and perks we offer:
- Competitive salary and performance-based annual bonuses.
- Dollar for dollar 401K match of 6%.
- Flexible work hours, hybrid schedules with remote work options.
- Comprehensive health and wellness benefits package with an onsite and virtual medical facility.
- Company HSA contribution, Paid Parental Leave, and Employee Assistance Program.
- Company-paid Life Insurance, Accidental Death and Dismemberment, and Long-term Disability.
- PTO plan and Paid Holidays.
- Tuition Reimbursement.
- Company celebrations, summer parties, volunteer opportunities, games, prize drawings, and more!
WesTech considers all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At WesTech Engineering, LLC, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
WesTech Engineering, LLC AI Hiring
WesTech Engineering, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.