Artificial Intelligence/Machine Learning Lead - LLM/RAG

About the Role :

Youll lead model selection, integration, training workflows (RAG/fine-tuning), and scalable deployment of natural language and voice AI components.

This is a foundational hire for a technically ambitious platform.

Key Responsibilities

- AI System Architecture: Design the architecture of the AI-powered agent including LLM-based conversation workflows, voice bots, and follow-up orchestration.

- Model Integration & Prompt Engineering: Leverage APIs from OpenAI, Anthropic, or deploy open models (e.g., LLaMA 3, Mistral).

- Implement effective prompt strategies and retrieval-augmented generation (RAG) pipelines for contextual responses.

- Data Pipelines & Knowledge Management: Build secure data pipelines to ingest, embed, and serve tenant-specific knowledge bases (FAQs, scripts, product docs) using vector databases (e.g., Pinecone, Weaviate).

- Voice & Text Interfaces: Implement and optimize multimodal agents (text + voice) using ASR (e.g., Whisper), TTS (e.g., Polly), and NLP for automated qualification and call handling.

- Conversational Flow Orchestration: Design dynamic, stateful conversations that can take actions (e.g., book meetings, update CRM records) using tools like LangChain, Temporal, or n8n.

- Platform Scalability: Ensure models and agent workflows scale across tenants with strong data isolation, caching, and secure API access.

- Lead a Cross-Functional Team: Collaborate with backend, frontend, and DevOps engineers to ship intelligent, production-ready features.

- Monitoring & Feedback Loops: Define and monitor conversation analytics (drop-offs, booking rates, escalation triggers), and create pipelines to improve AI quality continuously.

Qualifications :

Must-Haves :

- 5+ years of experience in ML/AI, with at least 2 years leading conversational AI or LLM projects.

- Strong background in NLP, dialog systems, or voice AI preferably with production experience.

- Experience with OpenAI, or open-source LLMs (e.g. LLaMA, Mistral, Falcon) and orchestration tools (LangChain, etc.).

- Proficiency with Python and ML frameworks (Hugging Face, PyTorch, TensorFlow).

- Experience deploying RAG pipelines, vector DBs (e.g. Pinecone, Weaviate), and managing LLM-agent logic.

- Familiarity with voice processing (ASR, TTS, IVR design).

- Solid understanding of API-based integration and microservices.

- Deep care for data privacy, multi-tenancy security, and ethical AI practices.

Nice-to-Haves :

- Experience with CRM ecosystems (e.g. Salesforce, HubSpot) and how AI agents sync actions to CRMs.

- Knowledge of sales pipelines and marketing automation tools.

- Exposure to calendar integrations (Google Calendar API, Microsoft Graph).

- Knowledge of Twilio APIs (SMS, Voice, WhatsApp) and channel orchestration logic.

- Familiarity with Docker, Kubernetes, CI/CD, and scalable cloud infrastructure (AWS/GCP/Azure).