We're transforming how software gets built, with GenAI at the center of the development lifecycle. As an AI Transformation Engineer, AI transformation is your day-to-day focus. You'll design and build the agentic workflows, tools, and AI infrastructure our engineering teams rely on. You'll also take on business projects that need hands-on AI development, so the work stays varied. We're looking for a highly technical engineer who takes ownership of open-ended challenges, leads independently, and delivers results that move the needle.
What You'll Do
Drive AI transformation across the SDLC: design, build, and ship agentic workflows for planning, coding, testing, code review, and security remediation, from prototype through production
Lead business-driven AI projects when needed, from understanding the product need to delivering the solution
Find what's worth automating: work with engineering teams, architects, and product managers to find high-impact use cases
Engineer the harness: design and manage agent context, knowledge, tools, and scaffolding, and build evals that keep agents reliable at scale
Own outcomes, not just tasks: turn broad goals into clear metrics such as adoption, throughput, cycle time, and quality, and show what your work moved

Why You'll Love Working With Us
Real impact at scale: what you build changes how an entire organization develops software
GenAI at the core: we don't just use AI, we build it into our engineering DNA
Learning & growth culture: every engineer has a personal professional growth plan
We are hiring for various positions within the company, with some roles offered through outsourcing.
Join us and enjoy a dynamic and flexible work environment.

5+ years of hands-on software engineering experience (TypeScript/Node.js, Java or Python) Hands-on experience building and shipping LLM-powered systems or agentic workflows in production Strong harness engineering skills: hands-on with Claude Code, GitHub Copilot, or similar tools, with a deep understanding of how to build, scaffold, and manage agent context at scale Working knowledge of LLMs, RAG, tool use, MCP, evaluations, and human-in-the-loop patterns Solid experience with CI/CD, GitHub Actions, and cloud platforms, preferably Azure Proven ability to lead independently, bring structure to open-ended problems, and deliver measurable results Highly technical, yet not afraid to dig into the product and understand the business side Lives and breathes AI: stays current on the latest models, tools, and practices, and brings new ideas to the team Strong communication skills and a team-player mindset Advantages: Experience developing AI-SDLC / AI-PDLC solutions Experience building a company brain or organizational knowledge layer at scale Experience building agent verification solutions at scale, such as automated E2E testing with Maestro or similar frameworks Proven track record of leading cross-team projects and aligning stakeholders with different priorities Experience with agent orchestration frameworks, agent factories, or multi-agent systems Familiarity with observability tooling for monitoring agent and platform health