About the Role :

We're looking for a hands-on AI/ML Engineer to build and deploy intelligent AI solutions across our communication platform. This is an individual contributor role focused on LLM-powered applications, voice AI, NLP pipelines, and production-grade ML systems that enable automation and real-time decision-making.

Key Responsibilities :

- Build and deploy AI/ML solutions using LLMs, NLP, speech AI, and generative AI technologies.

- Develop LLM-powered features summarization, sentiment analysis, intent detection, auto-disposition, escalation tagging, and agent assistance.

- Build Agentic AI workflows, prompt pipelines, and retrieval-based systems using frameworks like LangChain.

- Work with speech technologies STT, TTS, Whisper, and voice intelligence solutions.

- Fine-tune, evaluate, and optimize AI models for accuracy, latency, and scalability.

- Build AI inference services and APIs using Python, FastAPI, Docker, and cloud-native technologies.

- Develop and maintain ML pipelines for data processing, training, evaluation, and continuous improvement.

- Integrate AI models into product workflows communication platforms, CRM systems, dialers, and automation.

- Optimize AI systems for high-volume production environments, focused on performance and reliability.

- Work with PostgreSQL, Redis, Kafka, and build scalable data workflows.

- Implement secure, compliant AI data handling, retrieval, and access control.

- Collaborate with product, engineering, and business teams as a hands-on builder of AI-driven features.

Requirements :

- 4 to 7 years of AI/ML engineering experience, with production-grade AI application development.

- Strong hands-on experience with LLMs, Generative AI, NLP, and conversational AI systems.

- Experience building Agentic AI applications and voice bot solutions highly preferred.

- Proficiency in Python and building AI services with FastAPI or similar frameworks.

- Hands-on experience with LangChain, HuggingFace, Whisper, GPT, or equivalent technologies.

- Strong understanding of NLP concepts text classification, summarization, sentiment analysis, speech processing, emotion detection.

- Experience deploying ML solutions with Docker, Kubernetes, CI/CD, and cloud platforms.

- Familiarity with PostgreSQL, Redis, Kafka, or RabbitMQ.

- Strong individual ownership mindset, comfortable working independently in a fast-paced startup environment.

Good to Have :

- Experience with multilingual AI models, especially Indian languages.

- Exposure to Rasa, Coqui TTS, speech emotion recognition, or conversational intelligence platforms.

- Prior experience in SaaS, contact center, CRM, dialer, or customer communication platforms.