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About This Role
Job Description:
Position Summary
This role is responsible for partnering directly with business functions across the organization to identify, design, build, and deploy AI\-powered solutions that improve productivity, automate business processes, and accelerate decision\-making.
The ideal candidate is an engineer who enjoys solving real business problems, rapidly building prototypes, and delivering production\-ready AI applications. This individual will embed with teams across Finance, Human Resources, Supply Chain, Operations, Sales, Engineering, Advanced R\&D, Customer Support, and other functional areas to understand their workflows and implement practical AI solutions that create measurable business value.
This position requires a combination of software engineering, product thinking, and strong collaboration skills. The successful candidate will help drive enterprise AI adoption while establishing reusable patterns and best practices that scale across the organization.
Key Responsibilities
AI Solution Development
- Partner with business stakeholders to understand workflows, pain points, and opportunities for AI\-enabled automation.
- Design, develop, test, and deploy AI\-powered applications, assistants, agents, and workflow automations.
- Rapidly build proof\-of\-concepts and minimum viable products to validate business value.
- Iterate quickly based on user feedback and operational needs.
- Develop reusable components and implementation patterns that can be leveraged across multiple business functions.
Application Development \& Integration
- Build full\-stack applications using modern software development frameworks and AI technologies.
- Integrate AI solutions with enterprise platforms including Microsoft 365, SharePoint, Oracle, Salesforce, Jira, and other internal systems.
- Assist with deploying and supporting AI applications in cloud\-based environments.
AI Platforms \& Engineering
- Build solutions utilizing Large Language Models (LLMs), AI agents, Retrieval\-Augmented Generation (RAG), Model Context Protocol (MCP), and other emerging AI technologies.
- Evaluate AI applications for accuracy, reliability, usability, and business effectiveness.
- Stay current on advances in AI technologies and identify opportunities to introduce new capabilities into the organization.
- Contribute to enterprise AI standards, development practices, and reusable frameworks.
Collaboration \& Business Partnership
- Embed within business functions to understand operational challenges and identify opportunities for AI transformation.
- Collaborate with functional areas to ensure solutions align with enterprise architecture and governance standards.
- Communicate technical concepts effectively to both technical and non\-technical audiences.
Support user adoption through demonstrations, documentation, and knowledge sharing.
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Required Qualifications
- Bachelor's or Master’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.
- 0–3 years of professional software engineering or application development experience.
- Strong analytical and problem\-solving skills. Ability to manage complexity, take initiative in problem solving, and think critically under pressure.
- Experience building applications utilizing Large Language Models (LLMs), AI APIs, AI agents, or similar generative AI technologies through professional work, personal projects, research, or open\-source contributions.
- Ability to quickly learn new technologies and adapt to evolving AI capabilities.
- Excellent communication and interpersonal skills.
Ability to work independently while managing multiple priorities in a fast\-paced environment.
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Preferred Qualifications
- Experience with OpenAI APIs, ChatGPT Enterprise, Azure OpenAI, Anthropic Claude, Gemini, or similar enterprise AI platforms.
- Experience with agent frameworks such as OpenAI Agents SDK, LangGraph, CrewAI, or similar orchestration frameworks.
- Familiarity with Retrieval\-Augmented Generation (RAG), vector databases, semantic search, and Model Context Protocol (MCP).
- Experience with Azure, AWS, or GCP cloud platforms.
- Experience with Docker, GitHub, CI/CD pipelines, or Kubernetes.
- Experience developing internal productivity tools or workflow automation solutions.
Contributions to open\-source projects, hackathons, AI competitions, or personal AI projects that demonstrate technical curiosity and initiative.
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What Success Looks Like
- High\-value AI solutions are delivered that improve productivity and streamline business processes across multiple functional areas.
- AI applications are adopted by business teams and deliver measurable operational value.
- Reusable AI components, workflows, and engineering patterns accelerate future solution development.
- Business stakeholders view the engineer as a trusted technical partner for identifying and implementing AI opportunities.
- The organization continues to expand its AI capabilities through practical, scalable, and secure implementations.
Nextpower offers a comprehensive benefits package. We provide health care coverage, dental and vision, 401(K) participation including company matching, company paid holidays with unlimited paid time off, generous discretionary company bonuses, life and disability protection and more. Employees in certain positions may be eligible for stock compensation. All plans are in accordance with relevant plan documents. For more information on Nextpower’s benefits please view our company website at www.nextpower.com . Pay is based on market location and may vary based on factors including experience, skills, education and other job\-related reasons. The annual salary range for this position is $102,000\-$115,000\. This role is also eligible for bonus and equity as part of the total compensation package
At Nextpower, we are driving the global energy transition with an integrated clean energy technology platform that combines intelligent structural, electrical, and digital solutions for utility\-scale power plants. Our comprehensive portfolio enables faster project delivery, higher performance, and greater reliability, helping our customers capture the full value of solar power. Our talented worldwide teams are redefining how solar power plants are designed, built, and operated every day with smart technology, data\-driven insights, and advanced automation. Together, we’re building the foundation for the world’s next generation of clean energy infrastructure.
Nextpower is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We are Nextpower
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Salary Context
This $102K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NextPower, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($108K) sits 50% below the category median. Disclosed range: $102K to $115K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NextPower AI Hiring
NextPower has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fremont, CA, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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