AI Transformation Engineer

Requirements:
6-8+ years of hands-on experience in senior software engineering positions.
Hands-on experience building and shipping agentic workflows or LLM-powered systems in production, not just prototypes.
Real experience working across agent harnesses (e.g., Claude Code and similar), and hands-on understanding of agent context: how it's built, scaffolded, and kept effective at scale.
Practical understanding of the actual building blocks: LLMs, RAG, tool use, evaluations, and human-in-the-loop patterns, not just theoretical familiarity.
Strong software engineering fundamentals: you can design, build, and operate a system end to end.
A demonstrated ability to work without a pre-defined spec: given a goal or a KPI, you can find the problem, scope the solution, and build it.
Product judgment: you can separate what's worth building from what isn't, and you know how to tell whether it actually worked.
Strong discovery skills: you can sit with engineers, map how work actually happens today, and identify where automation genuinely helps versus where it doesn't.
Strong communication skills: you can translate between technical tradeoffs and the people who need to understand the impact.
Advantages
A cost-aware mindset: you treat tokens like a resource to be managed, not an afterthought.
Experience building on top of Kubernetes, containers, or CI/CD infrastructure in production.
Experience with MCP-based tooling, agent orchestration frameworks, or LLM gateways.
Familiarity with observability tooling (e.g., Datadog) for monitoring agent or platform health.
Background in a fast-paced, high-growth B2B SaaS environment.
Exposure to health tech, healthcare institutions, or behavioral health.