Senior Software Engineer Applied AI

• 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

Nice-to-haves

• Real-time media or telephony experience
• Front-end / full-stack ability
• ML pipeline experience, vector search, or embeddings
• Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

• Streaming, low-latency speech-to-speech systems built on modern LLMs
• Telephony and real-time media (call control, live audio streaming)
• Audio handling and the quirks of real human conversation (interruptions, timing, noise)
• Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

• Wrapping nondeterministic models in deterministic control so they behave reliably in production
• Multi-model pipelines, prompt design, and cost/latency budgeting
• Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
• Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

• End-to-end ML pipelines: feature engineering, model training, and scheduled inference
• Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
• Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

• Infrastructure as code across multiple environments (we run on AWS)
• Managed compute, data, streaming, and orchestration services
• Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
• Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

• 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