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About This Role
About the Role
We are a marketing and technology agency that builds websites, custom applications, AI\-powered experiences, automations, integrations, and emerging technology solutions for our clients and internal teams.
We are looking for a Head of AI Solutions \& Creative Technology to lead how we imagine, architect, build, and deliver technology solutions.
This is not a traditional engineering management position.
We are looking for a creative technologist, solutions architect, AI\-native developer, and technical leader in one person.
You will work directly with clients to understand their businesses, identify problems and opportunities, design better processes, architect technical solutions, rapidly prototype ideas, and lead those solutions through production.
Our development philosophy is AI\-first. Approximately 90% of our development workflow leverages
AI\-assisted or "vibe coding" methodologies. We want someone who knows how to push AI development tools extremely hard while also having the technical depth to get under the hood, understand the underlying code, debug complex problems, challenge poor AI\-generated architecture, and manually engineer a solution when necessary.
The right person will be as comfortable brainstorming an innovative client experience as they are reviewing an API integration, database structure, application architecture, or pull request.
What You'll Own
Client Discovery \& Solution Definition
You will work directly with clients, executives, marketers, operators, and internal teams to understand what they are trying to accomplish.
You will:
- Lead technical and solution discovery sessions with clients.
- Translate loosely defined business challenges into clear technical opportunities.
- Ask the questions clients may not know they need to answer.
- Understand current business processes, systems, workflows, pain points, and constraints.
- Map current\-state and future\-state business processes.
- Identify opportunities to improve customer experiences and internal operations.
- Recommend solutions that may include AI, automation, SaaS platforms, integrations, custom development, or combinations of each.
- Help clients think beyond their original request when a better solution exists.
- Clearly communicate technical concepts to nontechnical stakeholders.
You should be able to take a client from:
"We know we have a problem"
to:
"Here is the process, here is the solution, here is how we will build it, and here is what success looks like."
Solutions Architecture
You will own technical solution design before development begins.
Depending on the project, this may include:
- Application architecture
- Business logic
- User experiences and workflows
- Data architecture
- Databases
- APIs
- Webhooks
- Third\-party integrations
- CRM integrations
- AI models and agents
- Authentication and permissions
- Automation workflows
- Cloud infrastructure
- Security considerations
- Reporting and analytics
- Deployment architecture
- Technical requirements
- Acceptance criteria
You should know how to balance what is technically possible with what actually makes sense for the client's business, budget, timeline, and goals.
AI\-Native Development
AI\-assisted development is a core part of how we operate.
You will be expected to use modern AI development tools to dramatically accelerate the development lifecycle, including tools such as:
- Claude Code
- OpenAI Codex
- Cursor
- Replit
- GitHub
- AI coding agents
- Low\-code and automation platforms
- Emerging AI development tools
We want someone who naturally asks:
"How can we build and test this in days instead of months?"
You will rapidly create:
- Working prototypes
- MVP applications
- Internal tools
- Client portals
- Web applications
- Websites
- AI\-powered experiences
- AI agents
- API integrations
- Workflow automations
- Proofs of concept
However, we are not looking for someone who blindly accepts what an AI coding tool produces.
You need to understand software engineering deeply enough to recognize when AI\-generated code or architecture is wrong.
When necessary, you must be able to get under the hood and troubleshoot:
- Frontend code
- Backend code
- APIs
- Databases and SQL
- Authentication
- Application architecture
- Git and version control
- Cloud deployments
- Logs
- Performance issues
- Security vulnerabilities
- Integration failures
- Data issues
- Complex bugs
Our philosophy is:
AI first. Engineer when necessary. Outsource strategically. Ship quickly.
Creative Technology
Creativity is a major part of this role.
We do not simply want someone who can build what they are told to build.
We want someone who can invent the solution.
You should constantly be thinking about new ways technology, AI, automation, data, design, marketing, and customer experience can work together.
You will collaborate with marketing strategists, creatives, designers, developers, account teams, and clients to imagine solutions that may not exist yet.
You should be excited about turning ideas into working prototypes and demonstrating what is possible.
We want someone who hears:
"Wouldn't it be cool if..."
and starts thinking about how to make it real.
Business Process \& Automation
Technology should improve how a business operates, not simply digitize a bad process.
You will be expected to:
- Understand how clients currently operate.
- Map workflows across departments and systems.
- Identify bottlenecks and unnecessary manual processes.
- Redesign workflows before automating them.
- Identify where AI can replace or augment repetitive work.
- Determine where human involvement is still necessary.
- Design data flows between platforms.
- Architect automation strategies.
- Recommend appropriate SaaS and technology platforms.
- Build integrations between existing systems.
Experience with platforms such as CRM systems, marketing automation platforms, n8n, Zapier, Make, GoHighLevel, APIs, webhooks, and similar technologies is highly valuable.
Build vs. Buy vs. Outsource
You will own the decision around how a technical solution should be delivered.
That may mean:
Build it yourself.
Build it with AI.
Configure an existing platform.
Integrate multiple platforms.
Assign it to another developer.
Hire a specialized contractor.
Outsource an entire component.
You will be responsible for making that decision based on speed, quality, scalability, maintainability, risk, cost, and client value.
Technical Leadership
You will lead the technical resources required to deliver our projects.
This includes:
- Managing freelance and contract developers.
- Identifying specialized technical resources when needed.
- Creating technical scopes and requirements.
- Estimating development effort.
- Breaking solutions into executable workstreams.
- Assigning development work.
- Reviewing code and architecture.
- Establishing technical standards.
- Reviewing AI\-generated development.
- Managing technical QA.
- Ensuring documentation is completed.
- Overseeing deployments.
- Troubleshooting complex issues.
- Holding technical partners accountable for delivery.
This is a player\-coach position.
We expect you to lead development while remaining highly hands\-on.
Client Leadership
You will often be the senior technical voice in the room with our clients.
You should be comfortable:
- Running client workshops.
- Participating in sales and discovery meetings.
- Presenting proposed technology solutions.
- Whiteboarding business processes.
- Explaining architecture in plain English.
- Discussing tradeoffs with executives.
- Providing technical estimates.
- Identifying risks.
- Challenging assumptions when necessary.
- Helping clients understand what is possible with AI and emerging technology.
- Becoming a trusted technology advisor to our clients.
Strong communication skills and executive presence are extremely important.
Agency \& Commercial Mindset
We are an agency, so technology must ultimately create value for our clients and our business.
You should naturally consider:
- What business outcome are we trying to create?
- What does the client actually need?
- What is the simplest effective solution?
- How quickly can we deliver it?
- What will it cost us to build?
- What should we charge for it?
- How maintainable will the solution be?
- What risks are we creating?
- Can AI reduce development time?
- Can existing technology eliminate unnecessary custom development?
- Can this solution become reusable intellectual property?
- Can we turn what we learn into additional opportunities for the client?
We value business judgment as much as technical knowledge.
What We're Looking For
The strongest candidates will have a combination of experience across:
- Solutions architecture
- AI\-assisted software development
- Full\-stack application development
- Creative technology
- Business process design
- Automation
- Systems integration
- API development
- Web development
- Client consulting
- Technical discovery
- Product thinking
- Rapid prototyping
- Technical project leadership
- Contractor or development team management
You do not need to be the world's foremost expert in every technology we use.
You do need to be technically strong enough to understand the architecture, evaluate the work, troubleshoot when necessary, and know when something is being built incorrectly.
You May Be a Great Fit If...
You are someone who:
- Is obsessed with AI\-assisted development and emerging technology.
- Uses AI as a force multiplier rather than a replacement for technical understanding.
- Can rapidly turn ideas into working products.
- Thinks creatively about technology.
- Loves solving complicated business problems.
- Can move between strategy and execution.
- Understands both software and business processes.
- Is comfortable working directly with clients.
- Can communicate with both developers and CEOs.
- Likes building more than managing meetings.
- Can operate effectively with incomplete requirements.
- Enjoys experimenting.
- Is constantly evaluating new AI tools.
- Knows when to build something and when not to.
- Can manage contractors without becoming dependent on them.
- Takes ownership of the final outcome, not just the code.
What This Role Is Not
This is not a traditional IT leadership role.
This is not a position focused primarily on managing a large engineering team.
This is not a project manager role.
This is not a pure solutions consultant who hands specifications to someone else.
This is not a "vibe coder" who cannot understand the underlying technology when something breaks.
And this is not a developer who simply waits for someone else to define exactly what needs to be built.
We need someone who can discover, imagine, architect, build, lead, and deliver.
How We Define Success
You are successful in this role when:
- Clients trust you to help solve complex business and technology problems.
- Vague ideas become clearly defined solutions.
- Working prototypes are produced rapidly.
- AI materially reduces development time and cost.
- The right technologies are selected for the right problems.
- Development resources are used efficiently.
- Contractors deliver high\-quality work.
- Technical risks are identified before they become major problems.
- Projects move from concept to production quickly.
- Solutions are maintainable after launch.
- Our creative and account teams view you as the person who helps turn ambitious ideas into reality.
- Clients increasingly involve us in larger and more strategic technology initiatives.
The Person We're Looking For
At the simplest level, we are looking for a:
Creative technologist who can actually ship.
Someone who can spend the morning in a client strategy session, map a business process at noon, architect an AI\-powered application in the afternoon, create a working prototype using AI\-assisted development, review a contractor's code the next morning, and confidently present the solution back to the client's executive team.
If your first instinct when someone presents a difficult business problem is:
"I bet we can build something for that."
we want to talk to you.
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 321 The Agency, 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.
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.
321 The Agency AI Hiring
321 The Agency has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in FL, US.
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.
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