Remote job · IT and software
Senior Forward Deployed AI Engineer
In one line: Senior Forward Deployed AI Engineer is a remote role in the United States at a healthcare imaging technology company.
- Remote (US)
- A healthcare imaging technology company
- Posted 24 Sep 2026
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https://keywordise.com/mcpApply to Keywordise job kgtlgok2akAbout the role
An embedded AI engineering role that partners with clinical, product and operations teams to ship validated, governed and monitored AI features. The engineer owns each solution from intake through production and leaves reusable assets behind.
At a glance
- PayNot stated in the posting
- Remote scopeRemote, United States
- Posted24 Sep 2026
- Last checked19 Oct 2026
What you will do
- Decide whether to build, wrap or vet each initiative and set the validation bar it must clear
- Build the agentic layer including tool and function calling, RAG, MCP integrations and multi-step orchestration
- Architect on the shared AI platform covering model serving, foundation-model contracts, eval harness and monitoring
- Run initiatives through AI intake and risk categorization, leaving a reusable template, tool or reference architecture behind
What they look for
- Six or more years building product-grade software, including shipping AI-powered features to production
- Experience with LLM or imaging work using tool calling, RAG or agent orchestration
- Ability to evaluate models, reason about failure modes and answer performance questions with a method
- Product delivery instincts to scope fuzzy requests and translate needs into validated designs
- Strong Python plus practical cloud engineering and deployment, ideally AWS and Bedrock
Questions about this job
Is this job fully remote, and from where?
Yes, it is listed as remote, aimed at candidates in the United States.
What does this job pay?
The posting does not state a salary, so there is no figure to quote.
What experience is needed?
The posting asks for: six or more years building product-grade software, including shipping AI-powered features to production; experience with LLM or imaging work using tool calling, RAG or agent orchestration; ability to evaluate models, reason about failure modes and answer performance questions with a method.
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