Senior Voice AI Platform Engineer (Cost, Latency, Scale) Company: INTIME IT Services (INTIME Voice AI)

Location:
Remote. At least 4 hours of overlap with IST. Engagement: Full-time or part time (minimum 20 hours per week)

Experience:
5+ years in software engineering, including 2+ years on production voice AI or real-time audio systems Compensation: To be discussed, based on experience and engagement type (full-time or part-time)

About us
INTIME IT Services is an 11-year-old SAP and AI consulting company with operations in Kolkata, Dhaka, and Dubai. INTIME Voice AI is our real-time voice agent platform, live with paying enterprise and education clients. We are expanding across India, Bangladesh, Thailand, and the UAE. We need a senior engineer who has already scaled a voice AI system in production. The role You own the unit economics and real-time performance of every call. Your mandate: the lowest achievable cost per connected minute, voice-to-voice latency under 1 second, and 10 times the current concurrency, without breaking quality gates. What you will do Cost per minute Model the fully loaded cost per connected minute (telephony + STT + LLM + TTS + compute + bandwidth), reported per tenant, per language, and per region Build an interaction complexity router: deterministic responses, then cache, then a small or local model, then a fast cloud LLM, then RAG, then a premium reasoning model, picked per turn by complexity, latency budget, and cost Run break-even analysis for self-hosting STT, TTS, and LLM inference on GPUs (vLLM, TensorRT-LLM, Triton) against per-minute API pricing, and migrate where volume justifies it Reduce LLM token spend through prompt compression, context trimming, prefix and KV caching where the provider supports it, and response length control Route web and app voice traffic over WebRTC to skip PSTN and SIP charges Put a quality gate on every cost change: WER, task completion, and naturalness must hold Latency (target: p95 voice-to-voice latency under 1,000 ms) Define latency as the time from the callers end of speech to the first audible agent audio, measured server side and client side Own the latency budget per stage: endpointing at or below 200 ms,



final STT at or below 150 ms, LLM time to first token at or below 300 ms, TTS time to first byte at or below 150 ms, transport and jitter buffer at or below 150 ms Stream at every stage: incremental STT partials, token streaming from the LLM, and sentence-chunked streaming TTS Replace silence-timeout endpointing with semantic end-of-turn detection, using a VAD plus turn-detection model Run speculative execution on STT partials (intent prediction, memory and RAG prefetch, tool preparation), all cancellable and none irreversible Handle barge-in: detect caller speech, stop TTS playback, and cancel in-flight LLM and TTS work within 200 ms Keep persistent, warm connections to providers. Remove cold starts. Deploy media and inference in regions close to callers: India, Southeast Asia, and the Middle East Scale and reliability Load test with synthetic callers up to 10 times current peak concurrency. Find and remove bottlenecks. Autoscale media workers and inference for outbound campaign spikes Isolate tenants so one clients spike cannot degrade another clients calls Build provider failover with health checks and circuit breakers across 3 or more STT, TTS, and LLM providers, with no dropped calls Track SLOs for availability, latency, and call failure rate Cache and memory Semantic response cache with embedding similarity thresholds, keyed by tenant, persona, language, permissions, and knowledge version TTS audio cache for repeated phrases, keyed by text, voice, language, and prosody settings Session memory within a call and user memory across calls, with TTL, deletion, and consent controls Tool and API result cache with per-source TTL Hard rule: no cache hit crosses tenants, users with different permissions, or stale data Observability Per-call distributed tracing with OpenTelemetry,



including per-stage latency spans Live dashboards for cost per minute, p50/p95/p99 latency, cache hit rate, provider error rate, and concurrent calls Alerts on latency, cost, and provider anomalies You must have done this before Owned a production voice AI or real-time audio system with real customer traffic Reduced cost per minute or inference cost on a live system, with before and after numbers Brought voice-to-voice latency under or near 1 second in production, with measured results Built or deeply modified a streaming STT, LLM, and TTS pipeline, not only configured a no-code platform Run load tests and scaled for high concurrency Integrated 3 or more STT and 3 or more TTS providers Technical requirements Expert in asynchronous Python (asyncio) or Node.js/TypeScript. Go or Rust is a plus. WebRTC and WebSocket audio streaming, codecs (Opus, PCM, mu-law), and jitter buffers Redis, a vector database (pgvector, Qdrant, or Milvus), message queues (Kafka, NATS, or RabbitMQ) Docker, Kubernetes, and AWS, GCP, or Azure in multiple regions OpenTelemetry, Prometheus, and Grafana Strong plus LiveKit Agents, Pipecat, or a custom voice orchestration framework GPU inference serving (vLLM, TensorRT-LLM, Triton) SIP trunking and carrier cost optimization Experience at a voice AI, CPaaS, or contact center AI company Outcomes expected in 90 days Cost per connected minute cut to the lowest level that still passes quality gates. Target: 40 percent or more below the current baseline. p95 voice-to-voice latency under 1,000 ms in production Concurrency capacity up 10 times, proven by load test Semantic cache and audio cache live, with zero cross-tenant leakage Cost and latency dashboards live per tenant and per region Please do not apply if You have less than 2 years of hands-on production voice AI experience Your voice AI experience is limited to no-code platforms or demos You cannot share measured cost or latency results from past work Include: The largest voice system you worked on (calls per day, peak concurrency) Your best cost or latency reduction, with numbers Hours per week available, if applying part-time

📌 Senior Voice AI Platform Engineer (Kolkata)
🏢 Intime It Services
📍 Kolkata