Senior Software Engineer – Applied AI

AMC Health

Senior Software Engineer developing AI voice agents and ML systems for regulated healthcare. Collaborating across disciplines in real-time systems, LLM engineering, and cloud infrastructure.

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

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in building and operating production backend systems, with a strong focus on Python programming and the machine learning lifecycle. Capable of designing control systems for LLMs and effectively debugging production issues in a regulated environment.

Highest-signal resume keywords

Python ProgrammingLLM ExperienceProduction Backend SystemsDistributed SystemsMachine Learning Lifecycle

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Production InferenceDebuggingControl Systems DesignBatch-Analysis PipelinesMetric Anomaly Tracing

Soft Skills

Judgment in Model UsageDisciplined Change Management

Tools & Technologies

Cloud SystemsTelemetry Tools

Industry Keywords

Generative AIRegulated EnvironmentData Handling

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
Retirement plans
Paid time off
Flexible work arrangements
Professional development