Software Engineer - 2 (Voicebot)

Role overview

Software Engineer - 2 (Voicebot) at Ameyo

Employment details

• Location: Bengaluru [Karnataka] [India]

About the employer

• Exotel is a leading provider of AI transformation to enterprises for customer engagement and experience.
• With over 20 billion annual conversations across Omni channel, voice, agents, and bots, Exotel is trusted by more than 7000 clients worldwide, spanning industries such as BFSI, Logistics, Consumer Durables, E-commerce, Healthcare, and Education.
• Customer expectations are evolving, and businesses face the challenge of balancing the need for increased revenue, optimized costs, and exceptional customer experience (CX).
• Exotel steps forward as your transformative partner, offering an AI-powered communication solution to address all three!
• AI @ Exotel Voicebot The Voicebot team builds and operates Exotel's real-time voice AI product - production bots handling live phone conversations for enterprise customers.
• 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.
• We believe in measuring what matters: a good demo isn't the same as a good eval.
• The Role You'll be part of the team building and continuously improving Exotel's voicebot - from the model layer (fine-tuning, evals) to the live conversation experience (speech quality, latency, turn-taking).
• This is an engineering role first: you'll build, evaluate, and ship changes that directly improve call quality and business metrics in live customer deployments.
• What We Expect at This Level Independent execution.
• Given a scoped problem and an agreed approach, you take it to production on your own - build, eval, deploy, monitor - without needing to be unblocked daily.
• Deep ownership of eval frameworks and working knowledge of associated services/infra.
• You go deep on the AI side of the product - models, evals, speech quality - and know enough about the surrounding services to trace a live problem across the pipeline and see it through.
• What You'll Do 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.
• Ship changes into the live conversational pipeline, with instrumentation and alerting built in from day one.
• Take ownership across the SDLC for your changes: design (with a senior engineer), eval design, deployment, and monitoring.
• What You Bring 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.
• Strong Python; comfortable reading/writing production code, not just notebooks. 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.
• Strong analytical rigor - you instinctively ask "how do we measure this" before shipping a change.
• Good-to-have Experience with real-time audio/streaming systems and streaming vs. batch tradeoffs.
• Experience with agentic orchestration and tool-calling patterns for LLMs.
• Exposure to RAG patterns - embeddings, vector stores, retrieval strategies.
• Familiarity with self-hosting/serving open-weight models.
• Familiarity with observability for AI workloads - cost tracking, quality dashboards.
• Experience with multi-tenant SaaS constraints (per-tenant config, isolation).
• Prior experience specifically in voice AI / IVR / contact-center domains.
• How We Work You own it.
• Build, eval, ship, monitor - and stay on the hook when it's running live.
• There's no separate "MLOps team" to hand off to.
• You measure it.
• No model or prompt change ships without an eval story behind it.
• You collaborate.
• You'll work closely with the voicebot architecture/product team and field delivery engineers shipping to enterprise customers.
• Good ideas win regardless of source.
• You stay curious.
• New models and techniques land constantly - evaluating and benchmarking new options is part of the job, not a side project.
• Why Exotel Work on AI problems at real scale: live voice conversations, not offline batch jobs, for enterprise customers.
• Strong senior engineers to design with, and real ownership of what you build.
• A team that treats "does it actually work" as more important than "does it demo well." Opportunity to work across the full voicebot AI stack: LLMs, speech, real time orchestration, and the infra it runs on.