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About This Role
About ArchitectNow
For 18 years, ArchitectNow has helped businesses ship software that does the work. We are a Microsoft Solutions Partner, with active designations in Digital \& App Innovation (Azure), Data \& AI (Azure), and Infrastructure (Azure) — the practice areas we invest in most heavily and the work we do day in and day out. Across cloud\-native applications, AI, the Power Platform, Dataverse, and the managed cloud services that keep production environments running, our focus has always been getting hard things built, well, and on time.
What has changed is how we build them. We are in the middle of a fast, deliberate move into an AI\-first practice. Our engineers use Claude, GitHub Copilot, and a growing stack of internal AI tooling to put their experience to work on harder problems and ship stronger solutions faster than the industry standard. Eighteen years of engineering discipline applied to a new generation of tools — that combination is what our clients hire us for, and it is the era we are building toward.
We are fully remote across 14 states. We are small enough that the work you do is visible to leadership the same week you do it, and selective about who we hire because the team carries real weight from day one. There are no layers between an engineer and the client by design.
The work we do is for real businesses solving real problems. Right now those businesses are asking a harder question than they were two years ago: how do we weave AI into how the work actually gets done, not bolt it on as a demo. That is the work. And that is why this role exists.
The role
The job of a delivery manager looks different than it did 18 months ago. We are hiring for the version that comes next.
The AI Solutions Delivery Lead is a new role at ArchitectNow that combines what our Technical Delivery Managers have always done — own client engagements, run agile teams of senior engineers, drive outcomes — with the work of an AI\-fluent product manager and business analyst. You will turn client conversations into specifications our development teams can implement, using a Spec\-Driven Development (SDD) workflow we have built and continue to refine for AI\-augmented teams. You will run delivery for some of our largest engagements. And you will be a major contributor to how we rebuild our SDLC around AI.
If you are already using Claude or Copilot to compress half\-day tasks into twenty\-minute ones, this is the environment to do that work at scale.
What you will actually do
You will own delivery across multiple significant client engagements, typically our most strategic accounts. These are real software projects with real budgets and real deadlines. You will run agile process for each team, drive sprint planning and refinement, and sit in the client conversations that set direction.
A typical week. Tuesday morning, you are walking a client product director through what the system needs to do. By Tuesday afternoon you have drafted a feature specification with Claude as a thinking partner, run it through our SDD review, and handed it to the engineering lead. Wednesday brings standups across two teams, an unblock on an API contract, and a rewrite on a story that came back ambiguous. Thursday afternoon you are three hours deep in process work that does not get billed \- getting our internal AI agents to generate first\-pass test plans, or building a Claude skill that summarizes sprint reports for clients.
You will write a lot of specs \- better specs than most people write today, because AI gives you the time to think things through more carefully. You will run scrum for teams of senior engineers who are themselves heavy AI users; they do not need someone who tracks tickets, they need a partner who can engage substantively on an architectural decision and push back on a half\-formed user story. You will be on calls with C\-level clients walking them through progress and, at times, having the harder conversation about saying no. And you will be looking, constantly, at how we deliver \- where AI can take cost out, where it can shorten cycle time, and where a new tool changes what is possible.
Who we are looking for
To be clear on scope first: this is not a heads\-down software engineering position. Our AI\-augmented engineering teams handle the day\-to\-day code. This is a hybrid product owner, project manager, and business analyst role built for the AI era — someone who engages with multiple clients and runs delivery across multiple concurrent projects, using AI as a force multiplier to operate at that scale without losing depth on any single engagement.
That said, this is still a highly technical role, and a real software development background is a meaningful differentiator. We care less about which tech stacks you have worked in than about whether you have actually been hands\-on in software delivery — sitting with stakeholders on requirements, writing code, reviewing pull requests, debugging in production, and shipping things customers depend on. The more of the SDLC you have lived in, and the deeper your involvement at each stage, the more value you will bring to the work on day one.
And one trait matters as much as any of the above: we are looking for someone who communicates clearly, takes real ownership, and follows through on what they commit to. Our clients and partners come back to us, refer us, and stay with us through difficult projects because the people on our teams are people they actually want to work with — communicators who close loops, raise issues early, and do what they said they were going to do. AI is a tool we use heavily; it is not what the relationship is built on. The relationship is built on the people, and we hire for that with the same seriousness we hire for technical skill. This one is non\-negotiable.
Required
- Delivery management, technical project management, product ownership, or business analysis experience. Consulting or services background strongly preferred.
- Technical fluency to operate alongside senior engineers. You can read code, follow a system diagram, and engage substantively on API design or technical trade\-offs. Prior hands\-on software experience is the most common path to that fluency, but it is not the only one \- what matters is that you can hold your own on a technical conversation.
- Active, daily use of AI tools in your delivery work — Claude Desktop, Claude Code, GitHub Copilot, or comparable. You can walk us through how you use them, what you ask them to do, and where you have learned not to trust them.
- A working command of prompt design and context engineering. You have informed opinions on how to structure a prompt, what context to provide, when to decompose work into steps, and how to evaluate the result. Familiarity with system prompts, agents, skills, and MCP is a strong plus.
- A genuine interest in helping organizations improve through the disciplined application of AI. You treat AI as a craft to develop and teach, not a checkbox to satisfy.
- Practical command of agile and scrum. You have run sprints, owned a backlog, and shipped software in a team\-based environment.
- Proven ability to manage multiple client engagements with competing priorities and shifting timelines.
- Excellent written and verbal communication. You write clearly, communicate proactively, and adjust your approach for your audience. Communication is not a bonus skill in this role — it is the role.
- Ownership and follow\-through as a defining trait. You close loops, raise issues early, and do what you said you were going to do. Non\-negotiable.
- Demonstrated ability to manage up. You bring leadership the context to act, not just the problem.
Bonus
- A background in software development. A coder, engineer, or hands\-on technical lead who has moved into a product owner, product manager, or delivery role is exactly the profile we are hoping to find. It is not required, but it is a big plus.
- Familiarity with Spec\-Driven Development as a practice. GitHub Spec Kit experience is a plus.
- Hands\-on time with Azure DevOps and GitHub for backlog management and CI/CD.
- Experience with Microsoft Azure, Power Platform, Dataverse, or Dynamics 365\.
- Scrum certifications (CSM, CSPO, SAFe, PSPO).
- A blog, an open\-source contribution, a side project, or a Claude skill, MCP, or agent you have built. Anything showing how you think about AI in practice.
The AI stack you will have
We treat AI tooling as core infrastructure, not a perk. We invest where the productivity gains are real and equip every delivery lead accordingly. In this role you will have:
- A Claude Max account
- GitHub Copilot
- Our internal library of Claude skills, MCP servers, and delivery agents — and a real opportunity to extend and build new ones
- Microsoft Copilot across the M365 suite
- Time and budget to experiment with whatever new tooling looks promising
Our hiring process
We have made deliberate changes to how we hire for this role, and we want to be transparent about all of them up front. Every step described below is required for any candidate moving forward in the process. There are no exceptions.
Eligibility and direct hire
This is a US\-based remote position. All candidates must be legally authorized to work in the United States indefinitely, without current or future sponsorship. We do not sponsor work visas, transfer existing sponsorships, or hire on any basis that would require sponsorship in the foreseeable future. This is also a direct\-hire position. We are not partnering with third\-party recruiters, staffing firms, contracting agencies, or consultancies on this role. Please apply directly; submissions from third parties will not be considered.
Identity verification
Candidates selected to move forward to an interview will be required to complete an identity verification check through Checkr, our third\-party verification provider, prior to continuing in the process. The rise of synthetic resumes and AI\-assisted candidate impersonation across the industry has made this step necessary, and we ask every candidate to complete it without exception. You can read more about the Checkr Identity Verification process at https://checkr.com/background\-check/identity\-verification.
Skills assessment
Candidates may be asked to complete an online assessment prior to interviewing. The assessment focuses on the capabilities this role actually requires: AI fluency, prompting technique, agentic development, and the ability to apply these tools to real work. It exists to give you a structured opportunity to demonstrate what your resume describes and to give us a consistent baseline across candidates.
Live technical interview
During the technical interview, we may use a comparable shared environment to work through a structured challenge together, with AI tools available and in use. This is designed to mirror the way work actually gets done at ArchitectNow, not to test memorization or trivia.
Background check (post\-offer)
Any candidate who receives and accepts an offer will be required to pass a full background check covering federal records and all fifty states, conducted by our third\-party provider Checkr, prior to their first day. This is standard for all ArchitectNow hires.
Pay: $110,000\.00 \- $145,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible spending account
- Health insurance
- Health savings account
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Work Location: Remote
Salary Context
This $110K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ArchitectNow, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 30% below the category median. Disclosed range: $110K to $145K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ArchitectNow AI Hiring
ArchitectNow has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $145K - $145K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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