Description

Job Description:
We are building the next-gen AI-native, multilingual, omnichannel communication platform for emerging markets. We are looking for a hands-on AI/ML & Data Engineering Lead who will own and deliver critical components of our agent assist stack — including Whisper/STT, LangChain-based summarization, emotion tagging, GPT integration, and scalable data pipelines.

Key Responsibilities
1- AI/ML + LLM/NLP
2- Lead implementation of LLM-based features: summarization, sentiment detection, auto-disposition, escalation tagging
3- Fine-tune and evaluate models (Whisper, GPT, HuggingFace, Rasa) for vernacular (Indian) language support
4- Build and deploy LangChain pipelines for prompt engineering, QA tagging, and agent assist
5- Prototype emotion recognition, contextual agent replies, and real-time assist layer
6- Build and maintain inference pipelines using FastAPI, Docker, Kubernetes
7- Integrate AI modules into core product features (Dialer, CRM sync, IVR)
8- Optimize model latency and deployment strategy for high concurrency environments
9- Architect scalable data pipelines using PostgreSQL, Redis, and Kafka
10- Build ETL/ELT workflows to support real-time analytics, dashboards, and feedback loops
11- Maintain secure, compliant data storage, retrieval, and access control pipelines (DPDP, GDPR-ready)
12- Collaboration & Leadership
13- Work closely with Product, Engineering, and UX to deliver features that directly impact agent productivity
14- Guide junior ML and data engineers; define and enforce coding/data standards
15- Contribute to AI strategy, model governance, and data infrastructure roadmap
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Required Skills & Qualifications:
Total experience of 4-8 years
•2-5 years of experience in ML/AI/Data Engineering with exposure to LLMs and production-
grade pipelines
•Hands-on with Whisper, LangChain, HuggingFace, or similar frameworks
•Solid Python (FastAPI preferred), SQL/PostgreSQL, and experience with RESTful APIs
•Proven experience with CI/CD, Docker, K3s/Kubernetes, Redis, Kafka/RabbitMQ
•Strong understanding of NLP/STT/TTS, summarization, and emotion tagging
•Ability to work in startup-paced environments with an ownership mindset
Bonus Skills
•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

Key Skills

Education

Any Graduate