Forward-Deployed Engineers Emerge as One of AI’s Fastest-Growing Jobs

Artificial intelligence is often feared for automating work. Yet as companies move from experimentation to the deployment of AI workflows, new job categories are emerging. Across the AI industry, startups and enterprise software firms are hiring engineers whose primary task is not building models but helping businesses deploy them inside real operations.

Monthly job listings for forward-deployed engineers increased by more than 800% between January and September 2025, according to the Financial Times, showing how quickly companies are creating roles focused on implementing AI systems inside enterprises. The surge reflects a broader shift in the AI economy as organizations discover that building powerful models is only the first step. Integrating those systems into workflows requires new types of technical teams.

Forward-Deployed Engineers Inside Businesses

The forward-deployed engineer combines software engineering with direct customer engagement, placing engineers inside client organizations to build and deploy AI solutions. The Financial Times reported that companies, including OpenAI, Anthropic and Cohere are expanding teams of forward-deployed engineers to accelerate the adoption of their AI platforms. Ramp describes forward-deployed engineering as building integrations and workflows that allow customers to use the technology in production.

The role has roots in Palantir, which pioneered the model by embedding engineers within customer organizations to implement software platforms and integrate data systems, according to A16Z. Those engineers often spend extended periods working alongside operational teams to connect data sources, design workflows and build custom applications on the platform.

Today, the approach is gaining ground in the AI industry as startups attempt to bridge the gap between model capabilities and enterprise deployment. In practice, forward-deployed engineers help companies integrate AI with internal databases, APIs and operational software, ensuring the technology can operate within existing infrastructure.

The role reflects a challenge facing AI adoption. Many organizations lack the data pipelines, system architecture or internal expertise needed to deploy AI tools on their own. Engineers embedded with customers help close that gap by translating models into production systems that companies can use within daily operations.

Rise of Hybrid Technical Roles

Forward-deployed engineers are only one example of a new set of hybrid technical roles emerging across the AI ecosystem. Companies are also hiring AI application engineers, solutions engineers and go-to-market engineers who work across product development, sales and customer deployment.

These positions reflect the reality that turning base models into usable products requires more than research. Engineers must integrate models with enterprise data, build applications on top of model APIs and adapt systems to specific industry workflows.

Andreessen Horowitz has described the trend as “services-led growth,” where startups deploy technically skilled teams to work directly with customers during early product adoption. Instead of relying on product-led growth models, companies provide implementation support to help organizations integrate AI systems into their operations.

This approach allows companies to learn from real deployments while improving their products. Engineers working with customers provide feedback about how models behave in production environments, which features are needed and where workflows break down.

The result is a workforce structure that blurs boundaries between engineering, consulting and product management. Engineers are increasingly expected to understand both technical systems and business operations.

For workers, the shift opens new career paths that combine technical expertise with operational skills. Engineers who can build systems, communicate with customers and translate AI capabilities into real-world applications are becoming central to the next phase of the AI economy.

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