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About This Role
AI Solutions Developer
Full-time · Remote (US)
P3 Adaptive is an AI and data consultancy built on Microsoft technologies: Power BI, Microsoft Fabric, Microsoft 365, and Azure. We're hiring a software engineer to build AI applications across client engagements, our own products, and reusable tools for our consultants.
In this role, you'll partner with clients and the consultants who know their data, work through the problem together, and build the software that solves it, whether that's an agent that answers questions about company data or an app that takes a repetitive task off someone's plate. Why this job is different
You'll know why you're building it. Every project starts with a client problem we think we can solve or a tool our own team needs. Each solution is built for that client's situation. If it stops making sense, we change course.
We're honest with clients. We don't sell anyone the wrong thing. Ever. When something isn't a good fit, we say so.
Direct client contact. You'll be on the call with clients from the first conversation through go-live. Nobody in between translating requirements. Real conversations with real people about real problems.
AI is how we work. You'll use coding agents like Claude Code and Codex every day. They'll write a lot of the code. You pick the right tool for the job, decide what gets built, check that it works, and own what ships.
Remote, with a life. We're fully remote in the US and overlap for a few core hours on weekdays. Working faster doesn't mean working more. We mean it about having a life outside work.
What you'll actually do
The consulting work:
- Leading client conversations to understand the business problem behind the request, then translating it into what to build.
- Keeping projects on track: raising risks early, keeping each milestone demonstrable, and flagging scope changes the moment you see them.
- Training client teams so they can own and run what you build.
- Spotting the next problem worth solving for a client and bringing it to your account team.
- Demonstrating solutions to colleagues and clients. You'll explain how they work, answer questions about their limitations, and discuss what's needed to put them into use.
The engineering work:
- Building and maintaining AI applications and agents, taking features from a prototype through testing and deployment.
- Connecting applications to Power BI semantic models, Fabric data, and other business systems through APIs, with appropriate sign-in and access permissions.
- Investigating bugs and performance problems, checking AI responses against the underlying data, and improving application reliability.
- Creating examples, templates, and documentation so other people can use and maintain what you build.
- Trying new AI tools on practical problems and sharing what's worth using and why.
We're looking for someone who enjoys figuring things out and brings ideas of their own. You'll have support and code review from the team, with responsibility for the work you take on. Independent experimentation matters here, and so does knowing when to ask a question or work through a problem with someone else.
What you need
Consulting Skills
- You care as much about business impact as technical polish.
- You can translate a client's frustration into a technical and human solve.
- You're comfortable presenting to a C-suite or a front-line manager, with the professional presence clients trust with their business.
- Clear communication. You can explain a technical decision to another developer and describe what an application does to someone who doesn't write code.
Engineering Skills
- Three or more years of software development experience, including maintaining applications after launch.
- Experience building web applications with React and TypeScript, including connecting interfaces to APIs and working with application data.
- Hands-on experience developing software with agentic coding tools such as Claude Code or Codex. You can provide useful context, guide the work, review changes, and verify the results.
- Solid programming fundamentals. You're comfortable using Git, reading unfamiliar code, and tracing problems through an application.
- An understanding of Power BI and Microsoft Fabric concepts, including how data is organized and modeled for reporting and analysis. Hands-on experience with these products is welcome but isn't required.
- Familiarity with the Microsoft ecosystem and the basics of cloud services, authentication, and permissions.
Nice to have
Experience with Next.js, Python, Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, Azure OpenAI, Microsoft Graph, or MCP would be useful. Previous consulting work or experience building tools for other developers would also help.
What we offer
- Base salary: $125-150k
- Full benefits: Medical, dental, vision, life, 401k
- Remote work: US-based team. Your home office wherever makes you happiest
- Professional growth: Time built in to try new tools on real problems and share what you find. People here want to see you grow, and they'll jump in when you're stuck
Our values
We hire for the same five values in every role at P3:
- Reliable. You do what you said you'd do, and what you ship keeps working.
- Adaptable. The tools change every month. You change with them, and you change course when a project stops making sense.
- Proactive. You flag a problem the moment you see it, and you fix the small things before anyone has to ask.
- Invested. You care whether the client's business actually gets better.
- Direct. You say plainly what works, what doesn't, and what you don't know yet.
About P3 Adaptive
We've been modernizing organizations' relationship with data since 2010. We pair deep technical skill with business sense, and we skip the deck theater and the jargon. Our teams are small, senior, and hands-on, and work doesn't pass through a chain of handoffs.
We're building on top of the AI wave. If you want to build the AI that changes how companies use their data, we're ready to meet you.
P3 Adaptive is an equal opportunity employer. We make hiring decisions based on qualifications, merit, and business need. Period.
This position is open to people authorized to work in the United States. We don't sponsor work visas.
Salary Context
This $125K-$150K range is in the lower quartile 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 P3 Adaptive, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $125K to $150K.
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.
P3 Adaptive AI Hiring
P3 Adaptive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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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