Bringing healthcare home
Senior Software Engineer – Applied AI
Location
Virginia
Posted
1 day ago
Salary
0
Seniority
Senior
Job Description
Senior Software Engineer – Applied AI
AMC Health
• 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
Job Requirements
- 7+ years building and operating production backend systems
- 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
- Health insurance
- 401(k) matching
- Paid time off
- Flexible work arrangements
Related Guides
Related Job Pages
More AI Engineer Jobs
• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing • Design control systems around LLMs • 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
• 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
• 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
• Ship and debug code on a live, real-time voice pipeline • Design control systems around LLMs • 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
