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About This Role
Director of AI
PlanHub is the leading pre-construction SaaS platform and marketplace helping general contractors, subcontractors, and suppliers connect and grow their businesses. Built with tradespeople in mind, PlanHub is designed around the user workflow to help boost productivity, maintain deadlines, increase revenue, and create relationships. Easily post projects or submit bids with anytime-anywhere collaboration for every commercial construction trade.
We are seeking a Director of AI to provide senior-level leadership in executing the vision, strategy, and governance of artificial intelligence across the organization. This role emphasizes strategic direction, organizational leadership, and measurable business impact. The Head of AI will ensure the successful adoption and scaling of Generative AI and traditional ML within a robust AWS-based AI ecosystem.
What you will be doing:
- Define, execute, and own the organization’s AI vision, strategy, and long-term roadmap, ensuring alignment with business objectives and measurable outcomes
- Lead, build, and scale high-performing AI, ML, and data science organizations, establishing clear operating models, best practices, and governance
- Oversee the design and adoption of agentic AI and generative AI systems, including content understanding, information retrieval, content generation, and multi-agent workflows. This spans from ingestion and content management, model selection, indexing patterns, orchestration frameworks, evaluation frameworks, and ongoing monitoring, operations, and improvement.
- Own the strategic direction for ML-based recommendation and personalization systems, guiding initiatives across content discovery, ranking, personalization, and user engagement
- Lead the business and product with Data Science to understand network health and build data-driven systems to optimize liquidity, improve retention, and enter new markets
- Establish success metrics, experimentation frameworks (e.g., A/B testing), and continuous improvement processes for AI-driven products
- Oversee the organization’s AWS-based AI and ML platform, including governance of SageMaker, Bedrock, ML platforms, and deployment infrastructure
- Set standards for AI/MLOps, model lifecycle management, monitoring, cost optimization, and scalability, in partnership with Engineering and Platform teams
- Ensure AI solutions meet requirements for security, privacy, ethics, and regulatory compliance, driving responsible AI practices
- Act as the AI advisor to senior leadership, influencing product strategy, investment decisions, and external partnerships
What you will need to be successful:
- 10+ years of industry experience in AI / machine learning, with significant leadership responsibility
- Proven leadership of customer-facing recommendation systems, information retrieval, and personalization platforms
- Demonstrated experiencing leading the design, build, and operations of generative/agentic-AI products, including a strong understanding of LLMs, RAG, and agentic AI concepts
- High agency and a sense of urgency to create value for customers in an iterative, test-and-learn product development cycle
- Experience overseeing AI systems deployed on AWS
- Track record of leading cross-functional AI initiatives
- Ability to communicate complex AI topics to executive and non-technical audiences
- Experience aligning AI investments with measurable business outcomes
- Experience leading AI strategy at scale
- Familiarity with AI governance frameworks and regulatory landscapes
What's in it for you:
The opportunity to join PlanHub, named to the 2025 Inc. 5000 list of America’s fastest-growing private companies, marking our fifth consecutive year on the list—a testament to sustained growth and momentum. You can make an immediate impact as PlanHub moves to dominate the industry!
PlanHub offers:
- An awesome culture where you will be empowered, make an impact, and learn a ton
- Remote friendly
- Open time-off policy
- 401(k)/RRSP plan with a company match
*This position will be a remote position within the United States or Canada. Applicants must be authorized to work for any employer within the United States or Canada. We are unable to sponsor or take over sponsorship of an employment Visa at this time.*
*PlanHub is an equal opportunity employer. We are committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local laws.*
*PlanHub complies with all applicable laws governing nondiscrimination in employment in every location in which the company operates. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, benefits, training, and development.*
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PlanHub, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
PlanHub AI Hiring
PlanHub has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 16% 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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