Senior Forward Deployed AI Engineer (GenAI, AWS)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Forward Deployed AI Engineer (GenAI, AWS) based in the United States. This role is designed for a senior, hands-on engineer who wants to combine advanced AI engineering with direct ownership of business outcomes. You will work alongside enterprise customers to understand their operations from the inside, redesign critical workflows, and build production-ready generative AI systems that deliver measurable impact. Rather than relying on traditional requirements gathering, you will immerse yourself in real business functions before determining what to automate and how. You will develop agentic applications, LLM-powered workflows, and evaluation frameworks across financial services, insurance, healthcare, and related industries. Working in small, senior teams, you will collaborate with business leaders, operators, and technical stakeholders to drive adoption and continuous improvement. This remote-friendly opportunity offers the autonomy to shape solutions from first principles while remaining deeply involved in the code. Accountabilities: You will take ownership of the full journey from understanding a customer's operational challenges to building, deploying, and improving AI-powered solutions. Your responsibilities will include: - Understand customer operations firsthand: Spend the initial two to four weeks of an engagement performing the operator's role, learning the workflows, constraints, and practical challenges that shape the business function. - Learn new business domains quickly: Develop working expertise in areas such as insurance underwriting, healthcare revenue cycle management, claims processing, and asset management, engaging directly with the people responsible for these functions.

- Redesign business processes: Collaborate with operators and Forward Deployed Executives to rethink workflows from first principles, combining discovery, user research, technical design, and implementation planning. - Build production-grade GenAI systems: Develop and deploy LLM applications, agentic workflows, retrieval systems, structured-extraction pipelines, backend services, and supporting data infrastructure. - Establish rigorous evaluation frameworks: Build evaluation harnesses before implementing features, define measurable success criteria, instrument system performance, and use evaluation results to guide design decisions. - Deliver end-to-end engineering solutions: Write production code across backend services, data pipelines, and AI components, primarily using Python and TypeScript while adapting technologies to customer requirements. - Deploy and operate cloud-native systems: Deliver secure, observable, containerized, and maintainable solutions on AWS, or on GCP and Azure when required by the customer. - Improve and maintain industry blueprints: Start from existing, working AI solutions and incorporate lessons learned from customer engagements to strengthen reusable systems and accelerate future deployments. - Own measurable business outcomes: Partner with Forward Deployed Executives responsible for business unit KPIs and align engineering decisions with the same performance objectives. - Drive adoption and operational change: Ensure solutions become part of everyday workflows, working with users and stakeholders to address adoption barriers and improve business processes. - Collaborate with stakeholders at every level: Build credibility with customer engineers, operational teams, and senior executives, constructively challenge assumptions, and communicate trade-offs clearly. - Influence delivery commitments: Help define project scope, technical feasibility, and implementation priorities before commitments are made, taking responsibility for what will ultimately be built and delivered.