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Agentic AI Engineer (Clinical AI)
Jobverse.io
5 hours ago
Full-time
On-site
Irvine, California, United States
InnovoCommerce is building clinical AI solutions for the life sciences industry. The Agentic AI Engineer will build and operate the runtime for clinical AI agents, including agent execution, context management, model-provider integrations, clinical standards support, evaluation, and security.
Responsibilities
Own the agent loop end-to-end: tool dispatch, run limits, termination, retries, recovery after mid-run failure, and the append-only event log the loop runs on Design how context is built each turn: token budgets, prompt cache layout, in-loop compaction, and what enters by reference versus by value Build and maintain the provider layer: one neutral request and response shape with adapters for Anthropic, OpenAI-compatible, and self-hosted (vLLM/LiteLLM) endpoints, and close behavior gaps Define agents as configuration: manifests, capabilities, loadable skills, and a tool registry with effect classes, policy checks, and approvals for side-effecting actions Ground agents in clinical standards (e.g. CDISC) by wiring them into the loop as active context and tools Build the evaluation nets that gate every harness and model change: trace attributes, goldens, deterministic scorers, and regression checks on tool choice and output quality Harden the runtime: prompt injection paths, least-privilege sub-agents, and redaction of regulated content from logs and telemetry Commute to the office when necessary for in-person collaboration (SoCal location, but if you're in the Bay Area let's discuss)
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Own the agent loop end-to-end: tool dispatch, run limits, termination, retries, recovery after mid-run failure, and the append-only event log the loop runs on Design how context is built each turn: token budgets, prompt cache layout, in-loop compaction, and what enters by reference versus by value Build and maintain the provider layer: one neutral request and response shape with adapters for Anthropic, OpenAI-compatible, and self-hosted (vLLM/LiteLLM) endpoints, and close behavior gaps Define agents as configuration: manifests, capabilities, loadable skills, and a tool registry with effect classes, policy checks, and approvals for side-effecting actions Ground agents in clinical standards (e.g. CDISC) by wiring them into the loop as active context and tools Build the evaluation nets that gate every harness and model change: trace attributes, goldens, deterministic scorers, and regression checks on tool choice and output quality Harden the runtime: prompt injection paths, least-privilege sub-agents, and redaction of regulated content from logs and telemetry Commute to the office when necessary for in-person collaboration (SoCal location, but if you're in the Bay Area let's discuss)
#J-18808-Ljbffr