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About the Role : This is a foundational role blending applied machine learning, LLM integration, and modern data engineering to drive real-time decisioning and automation across our AI-native dialer platform. You'll lead the build-out of voice AI, agent assist, and multilingual LLM features for contact centers. Key Responsibilities : - Lead implementation of LLM-based features summarization, sentiment detection, auto-disposition, escalation tagging - Fine-tune and evaluate models (Whisper, GPT, HuggingFace, Rasa) for vernacular (Indian) language support - Build and deploy LangChain pipelines for prompt engineering, QA tagging, and agent assist - Prototype emotion recognition, contextual agent replies, and a real-time assist layer - Build and maintain inference pipelines using FastAPI, Docker, and Kubernetes - Integrate AI modules into core product features (Dialer, CRM sync, IVR) - Optimize model latency and deployment strategy for high-concurrency environments - Architect scalable data pipelines using PostgreSQL, Redis, and Kafka - Build ETL/ELT workflows to support real-time analytics, dashboards, and feedback loops - Maintain secure, compliant data storage, retrieval, and access control pipelines (DPDP, GDPR-ready) - Work closely with Product, Engineering, and UX to deliver features that impact agent productivity - Guide junior ML and data engineers; define and enforce coding/data standards - Contribute to AI strategy, model governance, and data infrastructure roadmap Requirements : - 615 years of total experience - Agentic AI and Voice Bot development experience mandatory - 3+ years in AI with exposure to LLMs and production-grade pipelines - Hands-on with Whisper, LangChain, HuggingFace, or similar frameworks - Solid Python (FastAPI preferred), SQL/PostgreSQL, and RESTful API experience - Proven experience with CI/CD, Docker, K3s/Kubernetes, Redis, and Kafka/RabbitMQ - Strong understanding of NLP/STT/TTS, summarization, and emotion tagging - Comfortable working in startup-paced environments with an ownership mindset Good to Have : - Experience with multilingual models (Hindi, Tamil, Bengali) - Exposure to Rasa, Coqui TTS, or OpenWA integrations - Prior work in SaaS/Contact Center/Dialer/CRM ecosystems - Familiarity with speech emotion recognition or agent coaching models Why Join Us : - Shape the AI-native dialer experience for agents across India and overseas - Build with purpose a multilingual, affordable, fast-deploy SaaS platform for emerging markets - Work with modern tech : GPT, Whisper, LangChain, WebRTC, React, FastAPI (ref:hirist.tech) .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.