Software Engineer - 3 (Gen AI & Speech)
exotel
Job Description
What You'll Do
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Build and optimize LLM-powered pipelines for analyzing conversations at scale, managing cost, latency, and quality tradeoffs across model providers.
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Work on speech processing: improving transcription accuracy, handling multilingual audio, and solving real-world audio quality challenges.
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Design and build AI agents that reason over enterprise data and deliver actionable answers with appropriate guardrails.
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Develop and scale high-throughput, multi-tenant backend services with focus on reliability and performance.
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Own cost and observability for AI workloads: usage tracking, cost attribution, and quality monitoring. Model optimization and self-hosting are active areas of investment.
- Takeend-to-end ownershipof the software development lifecycle: requirements, design, development, testing, deployment, and monitoring.
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What You Bring
Must-have
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Bachelor's or Master's degree in Computer Science or equivalent.
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5-8 years of software engineering experience.
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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.
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Strong understanding of data structures, algorithms, multi-threading, and concurrency.
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Good understanding of software engineering concepts: design patterns, modularity, scalability.
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Experience with microservices architecture and distributed systems: designing, building, and operating them.
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Experience designing and developing RESTful APIs and async job-processing architectures.
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Production experience with databases, caching layers, message brokers, and search/analytics systems, including data modeling and scaling.
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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.
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Experience with major cloud platforms (AWS, GCP, or Azure).
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Experience with containers and orchestration (Docker, Kubernetes) and CI/CD pipelines.
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Strong analytical and problem-solving skills.
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Excellent written and verbal communication skills.
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Team player, comfortable working across teams (product, data, infrastructure) in a fast-paced environment.
Good-to-have
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Understanding of RAG patterns: embeddings, vector stores, retrieval strategies.
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Comfortable with Linux, shell scripting, and developing Linux-based applications.
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Familiarity with monitoring and observability tools such as Grafana, Kibana, Elasticsearch.
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Strong networking fundamentals: DNS, load balancing, proxies, firewalls.
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Experience with ASR/TTS engines: Whisper-family models, VAD, speaker diarization, alignment, and common failure modes.
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Experience working with audio pipelines: IP streaming, format handling, noise reduction, streaming vs. batch processing.
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Experience with graph databases and graph query languages.
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Familiarity with columnar analytics stores for large-scale analytical queries.
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Experience building multi-tenant SaaS with tenant isolation and per-tenant configuration.
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Exposure to LLM observability and cost tracking tooling.
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Experience self-hosting or fine-tuning open-weight models.
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