Exotel
Website:
exotel.com
Company:
https://www.linkedin.com/company/exotel-techcom-private-limited
Seniority: Mid-Senior level
Industries: Telecommunications and Software Development
Job details:
Software Engineer 3 - Gen AI & Speech
About Us
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!
Tech @ Exotel
Exotel engineering solves some really cool infrastructure level problems with the goal of ensuring no one misses a call or an SMS.
- Our focus is on building very fault-tolerant, loosely coupled, scalable and real-time distributed systems
- We emphasize a lot on clean abstractions of code, loosely coupled services and good coding practices
- We are very strong believers in "you built it, you own it!". And running a distributed system is very different from just building one!
- We are crazy about high availability
The Role
You'll be part of the team building Exotel's AI and speech platform, powering products like Conversation Quality Analysis, AI Voice Agents, and AI Chat Agents. This means working across large language models, speech processing, and distributed backend systems to process and understand millions of enterprise conversations at scale.
This is an engineering role first. You'll design, build, deploy, and operate production services, and own them end-to-end.
What You'll Do
- Build and optimize LLM-powered pipelines for analyzing conversations at scale, managing cost, latency, and quality tradeoffs across model providers.
- Work on speech processing: improving transcription accuracy, handling multilingual audio, and solving real-world audio quality challenges.
- Design and build AI agents that reason over enterprise data and deliver actionable answers with appropriate guardrails.
- Develop and scale high-throughput, multi-tenant backend services with focus on reliability and performance.
- Own cost and observability for AI workloads: usage tracking, cost attribution, and quality monitoring. Model optimization and self-hosting are active areas of investment.
- Take end-to-end ownership of the software development lifecycle: requirements, design, development, testing, deployment, and monitoring.
What You Bring
Must-have
- Bachelor's or Master's degree in Computer Science or equivalent.
- 5-8 years of software engineering experience.
- Proficiency in at least one backend language (Python, Java, Go, or similar). Ability to pick up new languages quickly; the language matters less than the engineering thinking behind it.
- Strong understanding of data structures, algorithms, multi-threading, and concurrency.
- Good understanding of software engineering concepts: design patterns, modularity, scalability.
- Experience with microservices architecture and distributed systems: designing, building, and operating them.
- Experience designing and developing RESTful APIs and async job-processing architectures.
- Production experience with databases, caching layers, message brokers, and search/analytics systems, including data modeling and scaling.
- Hands-on experience with LLM APIs (OpenAI, Gemini, Azure, or similar): prompt engineering, structured output, cost management. If you haven't worked with LLMs yet but have strong engineering fundamentals and a willingness to learn, that works too.
- Experience with major cloud platforms (AWS, GCP, or Azure).
- Experience with containers and orchestration (Docker, Kubernetes) and CI/CD pipelines.
- Strong analytical and problem-solving skills.
- Excellent written and verbal communication skills.
- Team player, comfortable working across teams (product, data, infrastructure) in a fast-paced environment.
Good-to-have
- Understanding of RAG patterns: embeddings, vector stores, retrieval strategies.
- Comfortable with Linux, shell scripting, and developing Linux-based applications.
- Familiarity with monitoring and observability tools such as Grafana, Kibana, Elasticsearch.
- Strong networking fundamentals: DNS, load balancing, proxies, firewalls.
- Experience with ASR/TTS engines: Whisper-family models, VAD, speaker diarization, alignment, and common failure modes.
- Experience working with audio pipelines: IP streaming, format handling, noise reduction, streaming vs. batch processing.
- Experience with graph databases and graph query languages.
- Familiarity with columnar analytics stores for large-scale analytical queries.
- Experience building multi-tenant SaaS with tenant isolation and per-tenant configuration.
- Exposure to LLM observability and cost tracking tooling.
- Experience self-hosting or fine-tuning open-weight models.
How We Work
- You own it. Design, code, deploy, monitor. DevOps is a culture, not a separate team.
- You lead. Mentor and own the output of a team of 4-6 engineers. Code reviews, design reviews, and growing people are part of the job.
- You collaborate. You'll work closely with product, data, and platform teams. Good ideas win, regardless of where they come from.
- You stay curious. New models, tools, and techniques emerge constantly. We expect you to evaluate and benchmark; staying ahead is part of the role.
Why Exotel
- Work on AI problems at real scale: billions of conversations, thousands of enterprise clients.
- High autonomy and ownership. Your decisions shape the product.
- A team that values engineering depth, not just shipping fast.
- Opportunity to work across the full AI stack: LLMs, speech, agents, infrastructure.
Click on Apply to know more.
