While most AI headlines focus on ChatGPT and other generative applications, a distinct category of infrastructure companies is creating what makes those technologies possible. These AI platform companies enable enterprises to deploy AI at scale and are scaling go-to-market (GTM) teams faster than traditional hiring playbooks can handle. As Mark Twain observed, “When everyone is looking for gold, it is a good time to be in the pick and shovel business.”
The majority of AI platform companies typically need GTM candidates with different qualifications than traditional SaaS sellers. Specifically, AI platform vendors typically sell to technical buyer personas such as ML engineers, data scientists, requiring specialized technical fluency from sales professionals.
Drawing on the research behind our new series, The Future of GTM in the Age of AI, and other sources, this blog covers the key trends impacting platform companies in the artificial intelligence sector, and how these are further shifting talent acquisition in this space:
How AI Platform Companies are Changing the Market
AI platform software companies build the tooling and infrastructure layer between foundation models and artificial intelligence applications. They are not building chatbots or AI copilots for end users; instead, they are enabling other organizations to develop, deploy, and operationalize AI systems. This category includes MLOps and model deployment platforms, vector databases, model observability tools, LLM orchestration frameworks, and GPU cloud and inference APIs.
This category is not vertical AI application vendors, hyper scalers like AWS and GCP, or physical infrastructure builders. That distinction is important to consider when hiring because the buyer persona, sales cycle, and required seller profile all differ significantly from both traditional enterprise SaaS and AI application companies. If you are hiring Sales Engineers (SEs) or Enterprise Account Executives (EAEs) using a standard SaaS playbook, you are likely looking for the wrong capabilities.
AI platform and infrastructure companies are disrupting traditional software stacks and redefining how enterprises extract value from automation. Most existing data workflows were never designed for AI workloads, creating both a significant challenge and a major opportunity in the market. Additionally, many enterprise teams leave as much as 80% of their proprietary knowledge undocumented, which limits effective AI implementation and forces engineers to rely on costly workarounds.
The GTM Motion Difference in AI
There are several factors that make the sales motion and ideal GTM strategy different for AI platform and infrastructure companies:
The Buyer is a Builder
The primary buyer at AI platform solutions is not a VP of Sales evaluating business value. Instead, it is an ML engineer, data scientist, or platform engineering lead evaluating technical merit. These buyers research tools independently, trial products before the sales demo, and make decisions based on hands-on experience rather than vendor pitches. They often filter out sellers who lead with a ROI script before demonstrating technical understanding.
Product-Led Growth is King for AI
According to Menlo Ventures’ 2025 State of Generative AI report, 27% of artificial intelligence application spending now flows through product-led growth (PGL) motions: nearly four times the rate seen in traditional software. When you factor in shadow AI adoption, PLG-driven tools may account for nearly 40% of market spend. Startups like Cursor have proven they can reach $200 million ARR without hiring a single sales rep, demonstrating that for platform and infrastructure companies, demand often exists before the sales team even shows up. GTM teams are then tasked with converting that product usage into long-term enterprise contracts.
The Roles You Should Be Hiring
Here are some of the key roles to consider hiring for AI platform and infrastructure go-to-market teams:
Sales Engineer
The most critical early GTM hire for many AI companies, Sales Engineers conduct live technical evaluations, support hands-on POCs, and defend product architecture decisions in front of senior IT decision-makers. A strong candidate for an artificial intelligence platform or infrastructure vendor is a former software engineer or data scientist who pivoted to a pre-sales role, or an SE with deep domain expertise in MLOps, data infrastructure, or developer tooling.
These professionals need hands-on experience with Python, Kubernetes, cloud platforms, and vector databases, along with the ability to read code and debug issues in real time. The key differentiator from a traditional AE is comfort engaging with engineering audiences, where questions often delve into implementation details.
Enterprise Account Executive
Enterprise AEs manage large, complex deals by navigating multiple decision-makers across lengthy sales cycles and negotiating contracts that often reach into the millions. At AI platform companies, they act as strategic partners to C-level executives, while also requiring a level of technical fluency with no equivalent in traditional SaaS.
Artificial intelligence solutions demand significant organizational transformation, security reviews, and procurement complexity, and the EAE needs to credibly articulate technical value to both business stakeholders and the IT decision-makers evaluating the product. The most successful candidates have often sold at similar deal sizes and price points before: someone whose entire experience is in $50K ACV deals will likely struggle with the longer cycles and executive stakeholder dynamics of a $500K platform contract.
Forward Deployment Engineer
The Forward Deployment Engineer (FDE) model has expanded rapidly as AI implementations have grown more complex. While Sales Engineers handle pre-sales validation and hand off after deal closure, FDEs begin their engagement post-sale and remain embedded throughout the implementation lifecycle by writing code, building integrations, and creating custom solutions that address the specific constraints of the customer’s environment.
They are a force multiplier for customer success in the same way SEs are for sales. The hiring triggers are specific: multiple enterprise customers requiring significant technical customization, product teams spending 30% or more of their time on customer-specific implementation issues, or early churn tracing back to implementation failure rather than product-market fit.
GTM Engineer
An emerging role at companies where the go-to-market motion is increasingly automated and instrumented, GTM Engineers (GTMEs) build and maintain the revenue tooling stack including CRM integrations, intent signal pipelines, outreach automation, and lead scoring systems rather than executing manual sales workflows. This is distinct from Developer Relations (community-focused, non-quota-carrying) and Sales Operations (process-focused, non-engineering-focused). GTMEs write code that scales the revenue motion, with the market reflecting the value of their contributions: job postings for the role grew 205% year-over-year from 2024 to 2025.
Customer Success Engineer
The Customer Success Engineer (CSE) is a hybrid role that combines technical depth with account management across post-sales onboarding and expansion. CSEs own the outcome after the contract is signed by troubleshooting integration issues, providing use case expansion guidance, and surfacing relevant product feedback. At AI platform companies where implementations are complex and churn is expensive, this role often influences whether a customer renews and grows or quietly divests after the first contract.
Where These Roles are Going
Based on our research into the future of GTM in the age of AI, the hiring picture for this segment will look meaningfully different within two to three years.
Technical sales is moving from a support function to the primary revenue-generating role. Today’s sales engineers and solutions architects report into a VP of Sales at most companies. We expect that to invert: soon, VPs of Sales Engineering will lead entire sales teams because customers favor technical credibility.
Sales teams will also become leaner and more productive. Each seller, supported by AI agents handling research, outreach, and pipeline hygiene, will carry significantly more revenue responsibility than their SaaS predecessors.
The go-to-market talent pipeline is also shrinking across the technology industry. Traditional SDR and BDR roles are being automated or eliminated. Entry-level job postings dropped year-over-year in 2025, while applications per opening surged. The replacement model is less like a sales development team and more like the associate programs at Bain or McKinsey: a longer-term investment in developing future technical sales leaders, rather than a pipeline of reps expected to deliver immediate ROI.
The Hiring Challenges for AI Platform Companies
The core problem is a talent pool that barely exists at scale. Most experienced enterprise sellers do not have ML infrastructure backgrounds. Most ML engineers who could credibly sell do not want to. The people who can architect a data pipeline and navigate enterprise software procurement are extremely rare, and the talent constraint is getting worse, not better.
Emerging vendors are also competing against established giants like AWS, Google Cloud, Microsoft Azure, OpenAI, and Anthropic for the same technical GTM profiles, often at a disadvantage on base salary. To secure top talent, smaller companies must leverage their mission, equity upside, and meaningful responsibilities hyper scalers cannot offer, while structuring packages and pitching the opportunity accordingly.
Time-to-productivity is also longer than in traditional tech. A strong enterprise sales candidate may need six to nine months to become productive at an MLOps platform because the domain, buyer, and technical language all require new fluency. That makes each wrong hire incredibly expensive, typically representing at least $200,000 in missed quarterly revenue.
The line between DevOps and GTM teams will start to blur as technical sales become more embedded in the revenue motion. This creates a risk of misaligning candidates with roles that doesn’t suit their strengths, while also overloading product engineers and developers during scaling efforts. Avoiding these pitfalls and securing a thoroughly qualified candidate requires a clearly defined seller persona at the outset of the hiring process.
Build the Team That Can Sell Your Solutions at Scale
The AI platform and infrastructure market will be won by companies that do two things well: create exceptional products and build GTM teams capable of credibly engaging the most technically sophisticated buyers in tech.
At Betts, we have spent over 15 years placing go-to-market talent at growing technology companies. As a result, we have the networks, market data, and specialized recruiting expertise to help you hire the technical sales roles this market demands. Contact us here to discuss how we can help you build the GTM team that wins in AI infrastructure.