Senior Software Engineer – Applied AI

AMC Health

Senior Software Engineer developing production AI voice agents and ML systems for healthcare. Collaborating across functions and handling production issues in a regulated environment.

Posted 7/15/2026full-timeRemote • Washington • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating production backend systems, with a strong focus on Python programming and hands-on experience with LLMs and generative AI. Proficient in managing the traditional machine learning lifecycle while ensuring compliance in regulated environments.

Highest-signal resume keywords

Python ProgrammingLLM ExperienceDistributed SystemsMachine Learning LifecycleRegulated Environment Compliance

ATS Keywords

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Hard Skills

Production Backend SystemsDebuggingControl Systems DesignMetric AnalysisProduction Inference

Soft Skills

Judgment in Model UsageAttention to Detail

Tools & Technologies

Cloud PlatformsTelemetry Tools

Industry Keywords

Generative AIReal-Time Voice PipelineData HandlingVendor Management

Tech Stack

Tools & technologies

CloudDistributed SystemsPython

About the role

Key responsibilities & impact

Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
Build and operate LLM evaluation and batch-analysis pipelines
Own traditional ML workflows from data to scheduled production inference
Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Requirements

What you’ll need

7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Benefits

Comp & perks

Health insurance
Professional development opportunities