neurons-lab.com · Poland ·
Design and ship a real-time voice copilot that transcribes vet-client calls, extracts fields, and fills the scheduling system via a Chrome extension, while meeting strict latency, accuracy, and cost targets.
blank-metal · Minneapolis, MN
Fulfillment IQ · Toronto, Ontario, Canada
practice-by-numbers · Gurugram, IND
The client is the largest US network of in-home veterinary hospice and end-of-life care. A major US private-equity sponsor drives the AI program and plans more projects across its portfolio.
We built a real-time voice copilot for their Veterinary Care Coordinators (VCCs). The copilot listens to live calls with pet families. It extracts appointment and clinical fields while the call runs. It fills the client's scheduling system through a Chrome extension. A second workstream, the Vet Visit Copilot, sends each vet an AI pre-visit briefing by email (Amazon SES).
Next is the production phase.
Stage: production SOW in executive alignment; start expected September 2026.
Duration: multi-month, with strong extension probability. 0.5 FTE minimum; ramp toward 1.0 FTE as production scales.
Why the role is open: the current architect moves to another strategic build. He stays at 0.15–0.2 FTE for supervision and knowledge transfer during ramp-up, so the new architect gets a structured handover.
Own the technical architecture and delivery of the voice copilot from validated PoC to production
Hit the bar this client tests against: latency, accuracy, concurrency, and cost
Keep expectations aligned: production polish is in scope now; protect the team from silent scope creep
Transfer knowledge continuously to the client's team and Neurons Lab engineers
Own the full pipeline: streaming speech-to-text, LLM field extraction, Chrome-extension delivery, and AWS infrastructure
Drive latency work: cut P95 from ~6s toward ~2s; remove post-processing corner cases (occasional ~1min lag on one field type)
Run model A/B tests (current pair: Claude Haiku vs GPT Luna) with golden-set evaluation for phonetic name and email accuracy
Own evaluation and cost: Langfuse traces, accuracy dashboards, real per-call cost from live calls, and an optimization plan
Harden for production: 5–10+ concurrent calls, strict data isolation between users, monitoring, alerting, and safe rollback
Ship epics end to end (example: the SES email briefing service); always keep a demo fallback so a live session never fails
Front technical discussions with a meticulous client; VCCs test edge cases and expect production quality
Present concrete system behavior, with numbers — this account rewards evidence, not slides
Hold the scope line: tie every feedback item to the SOW; route roadmap items (learning loop, persistent memory) to future phases
Keep internal discussions internal; all client-facing materials pass ADM review before sending
Lead the AI Engineer and the pod: set tasks, review output, unblock fast
Absorb the handover from the outgoing architect (0.15–0.2 FTE supervision window) and become independent fast
Run knowledge-transfer sessions; the project must have no single point of failure
Support the production SOW with estimates and architecture options when the account team asks
Real-time voice pipelines: streaming STT, turn handling, low-latency LLM inference — hands-on
LLM engineering: prompt engineering, structured extraction, guardrails, model A/B evaluation
Observability and evals: Langfuse or similar; golden datasets; latency, accuracy, and cost dashboards
AWS: Bedrock, serverless patterns, SES; token economics and per-call cost engineering
Full-stack pragmatism: strong Python; enough TypeScript / Chrome-extension knowledge to own the integration
Clear spoken and written English for demanding US executives
Contact-center / agent-assist patterns and metrics (handle time, cost per call, concurrency)
Production LLM operations: load testing, data isolation, incident handling
Nice to have: empathy-sensitive domains (healthcare, veterinary, insurance) and PE-sponsored rollouts
Key characteristics (screen for all four):
Voice AI in production — mandatory. Shipped at least one real-time voice or speech product to real users (agent assist, voice bot, live transcription copilot). Candidates will demo real artifacts at the interview.
6+ years hands-on AI/ML engineering, with strong recent LLM production practice
Latency and reliability record. Can show measured P95 reductions and concurrency fixes on a live system
Consulting / client-facing seniority. Calm and precise under detailed UAT scrutiny; manages expectations well
Nice to have:
Chrome extension delivery; telephony / streaming stacks (Amazon Connect, Twilio, LiveKit)
Langfuse in production
US client experience with Eastern-time overlap
rackspace · US-Work from Home