Software Engineer - 2 (Voicebot)

AboutUs

ExotelisaleadingproviderofAItransformationtoenterprisesforcustomerengagementandexperience.Withover20billionannualconversationsacrossOmnichannel,voice,agents,andbots,Exotelistrustedbymorethan7000clientsworldwide,spanningindustriessuchasBFSI,Logistics,ConsumerDurables,E-commerce,Healthcare,andEducation.

Customerexpectationsareevolving,andbusinessesfacethechallengeofbalancingtheneedforincreasedrevenue,optimizedcosts,andexceptionalcustomerexperience(CX).Exotelstepsforwardasyourtransformativepartner,offeringanAI-poweredcommunicationsolutiontoaddressallthree!

AI@ExotelVoicebot

TheVoicebotteambuildsandoperatesExotel'sreal-timevoiceAIproduct-productionbotshandlinglivephoneconversationsforenterprisecustomers.

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:agooddemoisn'tthesameasagoodeval.

TheRole

You'llbepartoftheteambuildingandcontinuouslyimprovingExotel'svoicebot-fromthemodellayer(fine-tuning,evals)totheliveconversationexperience(speechquality,latency,turn-taking).Thisisanengineeringrolefirst:you'llbuild,evaluate,andshipchangesthatdirectlyimprovecallqualityandbusinessmetricsinlivecustomerdeployments.

WhatWeExpectatThisLevel

Independentexecution.Givenascopedproblemandanagreedapproach,youtakeittoproductiononyourown-build,eval,deploy,monitor-withoutneedingtobeunblockeddaily.

Deepownershipofevalframeworksandworkingknowledgeofassociatedservices/infra.YougodeepontheAIsideoftheproduct-models,evals,speechquality-andknowenoughaboutthesurroundingservicestotracealiveproblemacrossthepipelineandseeitthrough.

WhatYou'llDo

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.

Shipchangesintotheliveconversationalpipeline,withinstrumentationandalertingbuiltinfromdayone.

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

WhatYouBring

Must-have

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.

StrongPythoncomfortablereading/writingproductioncode,notjustnotebooks.

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 you've been on the hook for something live.

Stronganalyticalrigor-youinstinctivelyask'howdowemeasurethis'beforeshippingachange.

Good-to-have

Experiencewithreal-timeaudio/streamingsystemsandstreamingvs.batchtradeoffs.

Experiencewithagenticorchestrationandtool-callingpatternsforLLMs.

ExposuretoRAGpatterns-embeddings,vectorstores,retrievalstrategies.

Familiaritywithself-hosting/servingopen-weightmodels.

FamiliaritywithobservabilityforAIworkloads-costtracking,qualitydashboards.

Experiencewithmulti-tenantSaaSconstraints(per-tenantconfig,isolation).

PriorexperiencespecificallyinvoiceAI/IVR/contact-centerdomains.

HowWeWork

Youownit.Build,eval,ship,monitor-andstayonthehookwhenit'srunninglive.There'snoseparate'MLOpsteam'tohandoffto.

Youmeasureit.Nomodelorpromptchangeshipswithoutanevalstorybehindit.

Youcollaborate.You'llworkcloselywiththevoicebotarchitecture/productteamandfielddeliveryengineersshippingtoenterprisecustomers.Goodideaswinregardlessofsource.

Youstaycurious.Newmodelsandtechniqueslandconstantly-evaluatingandbenchmarkingnewoptionsispartofthejob,notasideproject.

WhyExotel

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

Strongseniorengineerstodesignwith,andrealownershipofwhatyoubuild.

Ateamthattreats'doesitactuallywork'asmoreimportantthan'doesitdemowell.

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