Senior Forward Deployed AI Engineer (GenAI, AWS)

Based in the United StatesFull-TimeSenior

SalaryBase salary of up to $200,000 per year. On-target earnings (OTE) of up to $250,000, including applicable variable compensation.

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Job Details

At least 8 years of software engineering experience, including substantial hands-on responsibility for production code.
Remain an actively coding individual contributor.
Be willing to spend several weeks performing an operational role and immersing yourself in customer workflows before designing automation.
Demonstrate the ability to learn unfamiliar business domains quickly and collaborate with domain specialists.
Have experience shipping generative AI and LLM systems into production.
Have experience building or owning evaluation suites for nondeterministic systems and explaining evaluation methods and metrics.
Have strong software engineering fundamentals and the ability to become productive in unfamiliar codebases or programming languages.
Be proficient in Python and/or TypeScript.
Have experience delivering cloud-native applications on AWS, including containers, Kubernetes or Amazon ECS, infrastructure as code, CI/CD pipelines, and production operations.
Have a strong foundation in AI and machine learning, including model capabilities, limitations, and failure modes.
Have hands-on production experience with Claude Code and Cowork.
Be able to communicate with senior business leaders, CTOs, engineers, and operational teams across technical and business audiences.
Be comfortable with ambiguity, independent decision-making, and end-to-end ownership.
Be fluent in written and spoken English.

Immerse yourself in customer operations, including performing an operator's role for the initial two to four weeks of an engagement.
Develop working expertise in customer business domains such as insurance underwriting, healthcare revenue cycle management, claims processing, and asset management.
Redesign business processes with operators and Forward Deployed Executives through discovery, user research, technical design, and implementation planning.
Build and deploy production-grade LLM applications, agentic workflows, retrieval systems, structured-extraction pipelines, backend services, and supporting data infrastructure.
Build evaluation harnesses, define measurable success criteria, instrument system performance, and use evaluation results to guide design decisions.
Write production code across backend services, data pipelines, and AI components, primarily using Python and TypeScript.
Deploy and operate secure, observable, containerized, maintainable cloud-native solutions on AWS, GCP, or Azure as required by the customer.
Improve reusable industry blueprints using lessons learned from customer engagements.
Align engineering decisions with business unit KPIs and work with users and stakeholders to drive adoption and improve workflows.
Help define project scope, technical feasibility, and implementation priorities, and communicate trade-offs with customer engineers, operational teams, and senior executives.

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