Software Engineer 2 (Voicebot)

20billionannualconversationsacrossOmnichannelvoiceagentsandbotsExotelistrustedbymorethan7000clientsworldwidespanningindustriessuchasBFSILogisticsConsumerDurablesE-commerceHealthcareandEducation.

Customerexpectationsareevolvingandbusinessesfacethechallengeofbalancingtheneedforincreasedrevenueoptimizedcostsandexceptionalcustomerexperience(CX).ExotelstepsforwardasyourtransformativepartnerofferinganAI-poweredcommunicationsolutiontoaddressallthree!

TheVoicebotteambuildsandoperatesExotelsreal-timevoiceAIproductproductionbotshandlinglivephoneconversationsforenterprisecustomers.

We run real - time conversational pipelines end to end: speech recognition LLM reasoning/orchestration speech synthesis with tool-calling for backend actions.

We evaluate and swap models constantly across providers on cost latency and conversation quality not vibes.

Webelieveinmeasuringwhatmatters:agooddemoisntthesameasagoodeval.

YoullbepartoftheteambuildingandcontinuouslyimprovingExotelsvoicebotfromthemodellayer(fine-tuningevals)totheliveconversationexperience(speechqualitylatencyturn-taking).Thisisanengineeringrolefirst:youllbuildevaluateandshipchangesthatdirectlyimprovecallqualityandbusinessmetricsinlivecustomerdeployments.

Independentexecution.Givenascopedproblemandanagreedapproachyoutakeittoproductiononyourownbuildevaldeploymonitorwithoutneedingtobeunblockeddaily.

Deepownershipofevalframeworksandworkingknowledgeofassociatedservices/infra.YougodeepontheAIsideoftheproductmodelsevalsspeechqualityandknowenoughaboutthesurroundingservicestotracealiveproblemacrossthepipelineandseeitthrough.

Build and maintain LLM/speech eval frameworks for the voicebot task success hallucination instruction-following WER/latency barge-in and turn-taking quality across model and prompt changes.

Run fine-tuning experiments (full FT PEFT/LoRA/QLoRA) on open-weight models for domain specific voicebot tasks and produce the evidence for when fine-tuning beats prompting.

Benchmark LLMs and ASR/TTS engines on cost latency and quality across providers and self hosted options and make a clear recommendation from the data.

Diagnose and fix real production conversation failures bad turn taking misrecognition latency spikes prompt regressions using logs traces and eval data not guesswork.

Shipchangesintotheliveconversationalpipelinewithinstrumentationandalertingbuiltinfromdayone.

Take ownership across the SDLC for your changes: design (with a senior engineer) eval design deployment and monitoring.

Solid grounding in ANNs and transformer architecture attention tokenization decoding strategies enough to reason about why a model behaves a certain way not just call an API.

Hands-on experience with LLM evals: building or running eval harnesses LLM-as judge setups regression suites for prompt/model changes.

Hands-on experience with fine-tuning including PEFT/LoRA/QLoRA on at least one open weight model for a real task (not just a tutorial).

Working knowledge of speech/ASR - TTS evaluation WER latency diarization common failure modes in real (noisy accented multilingual) audio.

StrongPython;comfortablereading/writingproductioncodenotjustnotebooks.

2-4 years of software/ML engineering experience with at least some of it in a production system (not purely research/academic).

A track record of shipping and owning your own changes in production youve been on the hook for something live.

Stronganalyticalrigoryouinstinctivelyaskhowdowemeasurethisbeforeshippingachange.

Experiencewithreal-timeaudio/.

Experiencewithagenticorchestrationandtool-callingpatternsforLLMs.

ExposuretoRAGpatternsembeddingsvectorstoresretrievalstrategies.

Familiaritywithself-hosting/servingopen-weightmodels.

FamiliaritywithobservabilityforAIworkloadscosttrackingqualitydashboards.

Experiencewithmulti-tenantSaaSconstraints(per-tenantconfigisolation).

PriorexperiencespecificallyinvoiceAI/IVR/contact-centerdomains.

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on AI problems at real scale: live voice conversations not offline batch jobs for enterprise customers.

Strongseniorengineerstodesignwithandrealownershipofwhatyoubuild.

Ateamthattreatsdoesitactuallyworkasmoreimportantthandoesitdemowell.

Opportunity to work across the full voicebot AI stack: LLMs speech real time orchestration and the infra it runs on.