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About This Role
Non\-Union Position
Enterprise Applications; Portland, Oregon (US\-OR)
Hybrid schedule available for Oregon \& Washington residents.
Regular FT, Exempt
Posting \# 5667
About Us:
At NW Natural, we offer more than rewarding career opportunities and a vibrant, inclusive work culture. We invite you to join us in providing safe and reliable utility services and renewable energy to better the lives of the communities we serve. Our vision is to be the leader in service excellence, innovation and environmental stewardship for our customers, while building on our strengths as a trusted energy provider and environmental leader for our industry.
In addition to environmental stewardship, we’re also deeply committed to Diversity, Equity and Inclusion at NW Natural. Our DEI Council started 21 years ago, and today we continue to foster a culture where all employees can experience a sense of belonging, shared purpose and possibility.
NOTE:
Employees based at our Headquarters are required to work on\-site a minimum of three days per week. Specific in‑office days may vary by team and business needs. This hybrid schedule supports collaboration, connection, and engagement while also offering flexibility for remote work.
Position Summary:
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We're looking for a Technical Lead to join our AI \& Automation team. In this individual contributor role, you'll partner closely with the Enterprise Architecture team to shape how we design, estimate, and deliver AI and automation solutions across the organization. You'll bring deep hands\-on expertise in AI agent development, large language models (LLMs), and automation platforms — and you'll use that expertise to guide and inspire your teammates.
This isn't a management role — it's a technical leadership role. You'll lead with knowledge, collaboration, and craft.
Essential Duties \& Responsibilities:
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Technical Design \& Direction
- Collaborate with the AI Architect to conceptually design AI and automation use cases, including scoping, feasibility analysis, and effort estimation.
- Provide technical direction and guidance throughout the development lifecycle, from discovery through deployment.
- Evaluate emerging AI tools, platforms, and frameworks to recommend best\-fit solutions.
- Ensure that solutions are scalable, maintainable, and aligned with enterprise standards.
AI Agent \& LLM Development
- Design and build AI agents using modern agentic frameworks and orchestration patterns.
- Apply hands\-on expertise in large language models (LLMs) to develop, fine\-tune, prompt\-engineer, and integrate AI\-powered solutions.
- Define and enforce best practices for agent design, prompt management, and model evaluation.
Automation Solutions
- Build and contribute to automation solutions using platforms such as Microsoft Power Platform (Power Apps, Power Automate) and related tools.
- Identify opportunities to reduce manual effort and improve business processes through intelligent automation.
Mentorship \& Collaboration
- Serve as a technical mentor to other developers and automation engineers on the team.
- Share knowledge through code reviews, design discussions, documentation, and informal coaching.
- Foster a collaborative team culture grounded in continuous learning and technical excellence.
Communication \& Problem Solving
- Translate complex technical concepts into clear, accessible language for both technical and non\-technical stakeholders.
- Proactively identify and resolve technical blockers and ambiguities.
- Bring a solutions\-oriented mindset to every challenge.
Qualifications:
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Required Skills \& Experience
- 5\+ years of experience in software development, data engineering, or a related technical discipline.
- Proven experience building AI agents, including multi\-step reasoning, tool use, and orchestration frameworks
- Hands\-on experience with LLMs, including prompt engineering, RAG (Retrieval\-Augmented Generation), model integration, and evaluation.
- Experience with automation platforms, particularly Microsoft Power Platform (Power Apps, Power Automate) or comparable low\-code/no\-code tools.
- Proven proficiency in Python and/or other languages commonly used in AI development.
- Demonstrated ability to lead technical design sessions and produce clear technical artifacts (architecture diagrams, design docs, effort estimates).
Preferred Skills
- Experience with Azure AI services (Azure OpenAI, Azure AI Studio, AI Foundry, etc.).
- Familiarity with DevOps and MLOps practices.
- Experience integrating AI solutions with enterprise systems and APIs.
Competencies
- Collaboration: Works effectively across teams and disciplines; actively builds relationships.
- Communication: Clearly conveys technical ideas to diverse audiences — written and verbal.
- Problem Solving: Breaks down complex problems with structure and creativity.
- Mentorship: Invests in the growth of others through guidance, feedback, and knowledge sharing.
- Ownership: Takes initiative and sees work through from idea to impact.
Education
- Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience.
What we offer:
Health \& Wellness –
- Rich health insurance benefits with competitive employer contribution
- Free access to an online wellness resources platform
Work Life Balance \-
- Up to 23 Vacation Days
- 80 Hours of Sick Time
- 10 paid holidays and 3 floating holidays
- Flexible work arrangements
- 3 weeks paid parental leave
- Be part of a workplace where your voice matters. Engage with our Diversity, Equity \& Inclusion Council, and a variety of Employee Resource Groups that support community and growth.
- 1500 sq foot exercise facility and secure bike room
Financial \-
- Meaningful annual incentive bonus opportunity in addition to base salary
- Competitive 401K company contribution and match
- 15% discount on NW Natural stock through Employee Stock Purchase Program
- Up to $5250 a year in tuition reimbursement
- Wellness incentive program
Discounts \-
- 20% off natural gas service
- Up to 30% discount at NW Natural Appliance Center
- TriMet Pass for all HQ employees
- Generous discounts with Verizon \& AT\&T Wireless
Base salary range:$110,250\.00 \- $157,100\.00 per year, depending on qualifications
Annual Target Incentive: Level 3, 12\.0% target
*Targets are calculated using eligible earnings during plan year participation. Payout, if earned, is based on company and individual performance for each plan year and may range from 0% – 200% of target. Targets are subject to change in subsequent plan years. Must be employed by September 30 in order to receive any prorated payout.*
Application Process:To be considered for this position, submit a complete electronic application including cover letter and resume via our website.
https://www.nwnatural.com/about\-us/the\-company/careers
Deadline: 8/27/26
Disclosure: *We are a drug free workplace and we comply with Federal Drug Free Workplace Act and Department of Transportation regulations. NW Natural participates in E\-Verify. Individuals hired will have their Form I\-9 information submitted to E\-Verify. E\-Verify is an Internet\-based system that compares information from an employee’s Form I\-9, Employment Eligibility Verification, to data from U.S. Department of Homeland Security and Social Security Administration records to confirm employment eligibility.*
*E\-Verify Information*
NW Natural participates in E\-Verify. Individuals hired will have their Form I\-9 information submitted to E\-Verify. E\-Verify is an Internet\-based system that compares information from an employee’s Form I\-9, Employment Eligibility Verification, to data from U.S. Department of Homeland Security and Social Security Administration records to confirm employment eligibility.
*All applications must be submitted through NW Natural’s Electronic Application System. Resumes submitted via email, fax or mail will not be accepted in lieu of an electronic application.*
NW Natural is proud to be an equal opportunity employer. We welcome and embrace our candidates’ diversity and take affirmative action to employ and advance individuals without discrimination on the basis of race, color, sex, gender identity or expression, sexual orientation, religion, age, physical or mental disability, veteran status, pregnancy (including childbirth or related medical conditions), national origin, marital status, genetic information, and all other legally protected characteristics. We forbid discrimination and harassment in the workplace based on any protected status or characteristic. A criminal history is not an automatic bar to employment with NW Natural. Instead, we make individualized assessments regarding qualifications and backgrounds. NW Natural is also committed to providing reasonable accommodations for individuals with disabilities, individuals with sincerely held religious beliefs, and disabled veterans in our job application procedures. If you need assistance or an accommodation as part of the application process, please contact us at [email protected] or (971\) 979\-6341\.
*NW Natural does not accept unsolicited submissions or assistance from search firms for posted positions. Resumes submitted by search firms working under a valid and current written contract with NW Natural valid written Statement of Work in place for this position from NW Natural HR/Employment will be deemed the sole property of NW Natural. No fee will be paid in the event the candidate is hired by NW Natural as a result of the referral or through other means.*
Salary Context
This $110K-$157K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NW Natural, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($133K) sits 38% below the category median. Disclosed range: $110K to $157K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
NW Natural AI Hiring
NW Natural has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Portland, OR, US. Compensation range: $157K - $157K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
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