Senior Forward Deployed AI Engineer

The Forward Deployed AI Engineer embeds directly within active client and internal project teams to drive real-world adoption of AI engineering practices. Working as the delivery-facing counterpart to our internal AI R&D function, the role assesses each project's context, implements AI tools and agentic solutions where they create measurable value, and enables teams to sustain these practices independently after rollout. This role suits engineers who have spent 6+ years delivering production software and have grown into Senior or Tech Lead positions — people comfortable walking into unfamiliar contexts and adding value quickly. Responsibilities    Assess project context and needs prior to AI rollout, identifying where AI practices, tools, or agentic solutions create measurable value Translate project and business needs into actionable AI adoption opportunities and drive them from initial assessment through implementation and operationalization Help delivery teams integrate AI practices into their existing SDLC across development, testing, QA, and operational workflows Facilitate workshops, onboarding, and enablement sessions with engineers and project stakeholders Implement or adapt reference solutions and accelerators; provide post-rollout support and resolve adoption or technical issues Measure adoption, collect feedback, and feed lessons learned back into the AI R&D function and the AI SDLC Contribute reusable rollout playbooks, patterns, documentation, and guidance Operate across PO, PM, engineering, QA, and operational concerns as required by the rollout context, without becoming a permanent dependency for the project team Must Have Strong software engineering fundamentals and software delivery lifecycle experience as a Senior or Tech Lead: architecture, APIs, testing, delivery practices, maintainability, and production software; ability to quickly understand unfamiliar systems and transfer principles across languages and stacks Hands-on experience with AI-assisted software development (AI SDLC) Proven ability to understand and extract business and engineering requirements; consulting mindset: stakeholder interviewing, challenging assumptions, proposing pragmatic solutions Hands-on experience with AI agents and agentic workflows Experience with LLM APIs and model integration Experience with CI/CD and modern software delivery practices Practical understanding of evaluation and testing of AI systems Strong software quality and engineering practices Production support and troubleshooting experience Awareness of security, privacy, and responsible AI considerations Experience with observability and monitoring Strong communication skills across technical and non-technical audiences; comfort with frequent context switching; high ownership and autonomy; ability to operate with ambiguity Nice to Have LLM-based application development Retrieval-Augmented Generation (RAG) Tool/function calling and protocols…