Lead AI Solution Engineer

We are seeking a Lead AI Solution Engineer to embed AI into everyday engineering workflows and tooling to boost developer productivity across front-end and back-end teams. You will evaluate and deploy AI-assisted development approaches, automate lifecycle tasks, and coach teams on best practices. Apply to help accelerate delivery and reduce friction.

Define an AI enablement roadmap that improves engineering velocity across front-end and back-end teams Evaluate and deploy AI-assisted development tools for IDEs, code generation, and code understanding Design agentic workflows that automate repetitive engineering tasks and reduce developer friction Automate unit test generation, documentation creation, and pull request summarization using AI Integrate AI-driven analysis, linting, and vulnerability detection into CI/CD pipelines Establish prompt engineering guidelines and safe-use best practices for developers Coach engineers through workshops and office hours to drive effective AI adoption Partner with DevOps to operationalize AI tooling within existing pipelines and environments Measure impact using delivery metrics and qualitative feedback, then iterate on solutions Document standards, playbooks, and reusable workflows that scale across teams 5+ years of full-stack engineering experience focused on developer productivity Lead-level experience driving AI enablement or engineering optimization initiatives Proven leadership skills mentoring engineers and guiding adoption of new tooling Strong project execution skills delivering workflow and automation improvements end to end Hands-on AI & ML Strategy skills for practical engineering enablement Hands-on AI Agents Frameworks experience to automate development lifecycle tasks AI DIAL experience integrating AI capabilities into developer tools and processes Strong CI/CD knowledge to integrate automated checks and analysis into pipelines Strong IDE and version control skills with modern ecosystems and Git Strong coaching and stakeholder communication skills across engineering disciplines Upper-Intermediate English proficiency (B2) for collaboration and documentation Hands-on experience with LLM APIs and model providers Experience building RAG pipelines or fine-tuning workflows Familiarity with React, Next.js, and TypeScript contexts Familiarity with microservices, APIs, and database-driven back-end systems Experience with VS Code or JetBrains plugin ecosystems