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About This Role
ABOUT 321 THE AGENCY
321 is a full-funnel creative agency built for brands that operate in complex, regulated industries — healthcare, insurance, legal services, home services, entertainment, and travel and tourism. We're a remote, AI-first team that combines strategic creative with CRM infrastructure, performance marketing, and paid media to drive real, measurable business outcomes for our clients.
Technology isn't a support function at 321 — it's the foundation everything else runs on. Our CRM builds, paid media measurement, AI workflows, and client reporting all depend on systems that are well-architected, secure, and connected. The Director of Technology is the leader who makes that foundation work as one.
ABOUT THE ROLE
As Director of Technology at 321, you will lead the agency's technology function across internal systems, MarTech and CRM infrastructure, data and integrations, AI enablement, and information security. This is a senior leadership role with real strategic authority — you'll own the agency's technology roadmap, set standards for how our platforms are built and governed, and serve as the senior technical voice in client strategy, new business, and agency planning conversations.
The right person for this role thinks in systems, not tools. You understand that a CRM workflow is only as reliable as the integration feeding it, that clean data is the difference between real attribution and guesswork, and that AI only creates leverage when it's built into the way a team actually works. You're as comfortable reviewing a webhook payload or a High Level workflow as you are presenting a technology roadmap to agency leadership — and you lead a team that delivers both.
You'll also work in close coordination with 321 AIM, the agency's proprietary performance marketing platform, ensuring that its technology, data flows, and integrations stay aligned with the systems that power our client programs.
WHAT YOU'LL DO
Technology Leadership & Team Development
- Lead, manage, and grow the Technology department, including CRM/MarTech engineers, integration specialists, and systems administration
- Own the agency's technology roadmap, aligning platform investments with client delivery needs, agency growth goals, and operational efficiency
- Set the department's operating model: build standards, QA and testing practices, documentation requirements, change management, and support processes
- Conduct regular 1:1s and performance reviews; provide technical coaching and actively develop team capabilities
- Define role scopes, skill development paths, and staffing recommendations as the department grows to warrant additional headcount
- Serve as the escalation point for system outages, integration failures, data issues, and complex technical questions across the client portfolio
Agency Systems, Infrastructure & Security
- Own the agency's internal technology stack, including collaboration, project management, identity, file storage, and communication tools
- Manage user access, single sign-on, device policies, and account provisioning and offboarding for a fully remote team
- Establish and enforce information security policies, password and access standards, backup practices, and incident response procedures
- Ensure agency and client systems meet applicable privacy and compliance requirements, including HIPAA, TCPA, A2P 10DLC, CAN-SPAM, GDPR, and CCPA
- Manage technology vendors, licenses, contracts, and costs; evaluate build-versus-buy decisions
MarTech, CRM & Integration Architecture
- Set architecture and governance standards for client CRM and marketing automation programs built primarily on High Level, with additional work in Salesforce, Salesforce Marketing Cloud, and HubSpot
- Oversee integrations between CRMs, ad platforms, websites, call tracking, payment processors, and third-party systems using APIs, webhooks, and middleware such as Zapier, Make, or n8n
- Standardize snapshots, templates, naming conventions, tagging, and data hygiene practices across client accounts
- Lead technical discovery for new client onboarding and CRM migrations, including data mapping, cleansing, and cutover planning
- Review complex builds for scalability, reliability, and maintainability before launch
AI Enablement & Automation
- Champion the integration of AI across the agency — from internal operations and reporting to CRM personalization, conversational AI, and workflow automation
- Evaluate, pilot, and roll out AI tools and LLM-powered solutions, with clear standards for data privacy, accuracy, and appropriate use
- Build and maintain internal automations and AI-assisted workflows that reduce manual effort and improve delivery quality
- Stay current on emerging AI and MarTech capabilities and proactively introduce relevant innovations to the agency and its clients
Data, Analytics & Measurement Infrastructure
- Own the agency's data and measurement infrastructure, including conversion tracking, Google Tag Manager, GA4, server-side tracking, and offline conversion imports
- Partner with the Director of Digital Marketing to ensure lead source tracking, cost-per-lead reporting, and audience syndication between CRMs and ad platforms work reliably
- Build and maintain data pipelines and reporting infrastructure that feed client dashboards in Looker Studio, Power BI, or similar tools
- Establish data quality standards and monitoring across connected systems
Client Engagement & Agency Growth
- Serve as the senior technology authority in client strategy sessions, QBRs, and new business pitches — translating technical solutions into clear, business-outcome terms
- Contribute technology recommendations, solution architecture, and implementation estimates to agency proposals and RFP responses
- Partner with 321 AIM on shared clients and platform needs to ensure technology, data flows, and lead infrastructure are coordinated and mutually reinforcing
- Identify opportunities to productize repeatable technology solutions into new agency service offerings
WHAT YOU HAVE
- 8+ years of experience in technology, MarTech, or systems roles, with demonstrated depth in CRM architecture, integrations, and data infrastructure — agency experience strongly preferred
- At least 2–3 years in a department leadership, team lead, or senior manager role with direct reports
- Hands-on experience architecting and governing CRM and marketing automation programs in High Level is strongly preferred; Salesforce, Salesforce Marketing Cloud, and/or HubSpot experience also valued
- Deep experience with APIs, webhooks, and middleware (Zapier, Make, n8n, or similar), including troubleshooting integrations across multiple systems
- Strong command of conversion tracking and measurement infrastructure: Google Tag Manager, GA4, server-side tracking, and offline conversion imports
- Working knowledge of data pipelines, data warehousing, and reporting tools (BigQuery, Looker Studio, Power BI, or similar)
- Experience administering cloud-based business systems, identity and access management, and security for a remote team
- Working knowledge of privacy and compliance requirements affecting marketing technology in regulated industries, including HIPAA, TCPA, A2P 10DLC, CAN-SPAM, GDPR, and CCPA
- Practical experience deploying AI tools and LLM-powered workflows, with sound judgment on data privacy and appropriate use
- Scripting or development ability in JavaScript, Python, SQL, or similar is a strong plus
- Excellent communication skills — able to explain technical decisions clearly to clients, agency leadership, and non-technical teammates
- Strong organizational and prioritization skills with the ability to manage department operations across a remote, multi-client environment
- Platform certifications (High Level, Salesforce, HubSpot, Google, cloud or security certifications) are a plus
- Bachelor's degree in Computer Science, Information Systems, Marketing Technology, or a related field — or equivalent demonstrated experience
REPORTING STRUCTURE
The Director of Technology reports directly to the SVP of Strategy and is a member of the agency's leadership team. This role directly manages the Technology department, encompassing CRM/MarTech engineers, integration specialists, and systems administration. The Director of Technology maintains a working relationship with 321 AIM leadership to align platform technology and data infrastructure, and collaborates closely with the Director of Digital Marketing, Director of Client Strategy, and Creative Director on integrated account delivery.
COMPENSATION
Salary Range: $100K+ annually, based on experience
321 uses a structured compensation framework aligned to role scope, department impact, and agency revenue contribution. Salary placement within range is based on demonstrated experience, leadership depth, and technical breadth across CRM, integrations, data, and AI. Performance bonus eligibility applies at the director level.
321 BENEFITS
- 100% remote work environment
- Flexible scheduling with core collaboration hours
- Paid time off + agency-observed holidays
- Professional development and platform/tool access budget
- Collaborative, AI-forward team culture that values innovation and continuous learning
- Opportunity to build and lead the technology foundation of a high-growth agency operating at the intersection of performance marketing and marketing technology
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
321 The Agency AI Hiring
321 The Agency has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orlando, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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