For the last decade, engineering leaders have hired developers based on their ability to write software.
Today, a growing number of organisations are hiring people based on their ability to redesign how software gets built.
At Omnis, we’re seeing a significant increase in demand for engineers and engineering leaders with experience embedding agentic AI into the Software Development Lifecycle (SDLC). Not people who have simply experimented with ChatGPT or GitHub Copilot, but individuals who have successfully integrated tools such as Claude Code, Cursor and autonomous agents into real engineering organisations.
The reason is simple.
Most businesses have moved beyond asking whether AI can improve developer productivity. They already know it can.
The challenge now is operationalising it.
The Rise of the Agentic SDLC
The first wave of AI adoption focused on helping developers write code faster.
The next wave is fundamentally different.
AI agents are increasingly participating across the entire software delivery lifecycle, from requirements gathering and technical design through to implementation, testing, code review and documentation.
As a result, organisations are discovering that successful adoption isn’t really about the models themselves. It’s about the systems, processes and governance that sit around them.
Questions engineering leaders are now asking include:
- How should Claude Code fit into our development process?
- Where should human approval remain mandatory?
- How do we maintain quality as AI-generated code increases?
- How do we measure productivity gains accurately?
- What controls are needed to manage risk and security?
These are organisational challenges, not technical ones.
What We’re Seeing In The Market
Recently, we placed two senior engineering leaders into a global AI and data consultancy whose clients are actively looking to embed Claude Code into existing engineering workflows.
One had led an AI-first transformation across an organisation of more than 100 engineers, introducing AI tooling, measuring adoption, and implementing governance frameworks after identifying that while developer throughput increased by 40%, bug rates also rose as code volume accelerated.
The other had introduced AI-assisted engineering practices within a regulated financial services environment, defining coding standards, reusable agent capabilities, security controls and operational guardrails that enabled engineering teams to adopt AI consistently and safely.
Neither was hired because they knew how to write prompts.
They were hired because they understood how software delivery changes when autonomous systems become part of the engineering team.
Why Demand Is Growing
The companies moving fastest are no longer looking for AI enthusiasts.
They’re looking for people who have experience building:
- Agentic engineering workflows
- AI governance frameworks
- Quality and evaluation systems
- Human approval checkpoints
- Secure AI development practices
- Enterprise-wide adoption programmes
Much like cloud computing created demand for DevOps and Platform Engineers, agentic AI is creating an entirely new category of engineering expertise.
The organisations that succeed over the next few years won’t simply give developers access to AI tools.
They’ll redesign their SDLC around them.
And the people who know how to do that are quickly becoming some of the most sought-after talent in the market.