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Artificial Intelligence/Machine Learning Lead - LLM/RAG Job Opening In India, India – Now Hiring Begig


Job description

<p><p><b>About the Role : </b></p><p><p><b><br/></b></p>We're looking for an experienced AI/ML Technical Lead to architect and drive the development of our intelligent conversation engine.<br/><br/></p><p>Youll lead model selection, integration, training workflows (RAG/fine-tuning), and scalable deployment of natural language and voice AI components.<br/><br/></p><p>This is a foundational hire for a technically ambitious platform.<br/><br/><b>Key Responsibilities</b><br/><br/></p><p>- AI System Architecture: Design the architecture of the AI-powered agent including LLM-based conversation workflows, voice bots, and follow-up orchestration.<br/><br/></p><p>- Model Integration & Prompt Engineering: Leverage APIs from OpenAI, Anthropic, or deploy open models (e.g., LLaMA 3, Mistral).<br/><br/></p><p>- Implement effective prompt strategies and retrieval-augmented generation (RAG) pipelines for contextual responses.<br/><br/></p><p>- 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).<br/><br/></p><p>- 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.<br/><br/></p><p>- 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.<br/><br/></p><p>- Platform Scalability: Ensure models and agent workflows scale across tenants with strong data isolation, caching, and secure API access.<br/><br/></p><p>- Lead a Cross-Functional Team: Collaborate with backend, frontend, and DevOps engineers to ship intelligent, production-ready features.<br/><br/></p><p>- Monitoring & Feedback Loops: Define and monitor conversation analytics (drop-offs, booking rates, escalation triggers), and create pipelines to improve AI quality continuously.<br/><br/><b>Qualifications :</b><br/><br/><b>Must-Haves :</b><br/><br/></p><p>- 5+ years of experience in ML/AI, with at least 2 years leading conversational AI or LLM projects.<br/><br/></p><p>- Strong background in NLP, dialog systems, or voice AI preferably with production experience.<br/><br/></p><p>- Experience with OpenAI, or open-source LLMs (e.g. LLaMA, Mistral, Falcon) and orchestration tools (LangChain, etc.).<br/><br/></p><p>- Proficiency with Python and ML frameworks (Hugging Face, PyTorch, TensorFlow).<br/><br/></p><p>- Experience deploying RAG pipelines, vector DBs (e.g. Pinecone, Weaviate), and managing LLM-agent logic.<br/><br/></p><p>- Familiarity with voice processing (ASR, TTS, IVR design).<br/><br/></p><p>- Solid understanding of API-based integration and microservices.<br/><br/></p><p>- Deep care for data privacy, multi-tenancy security, and ethical AI practices.<br/><br/><b>Nice-to-Haves :</b><br/><br/></p><p>- Experience with CRM ecosystems (e.g. Salesforce, HubSpot) and how AI agents sync actions to CRMs.<br/><br/></p><p>- Knowledge of sales pipelines and marketing automation tools.<br/><br/></p><p>- Exposure to calendar integrations (Google Calendar API, Microsoft Graph).<br/><br/></p><p>- Knowledge of Twilio APIs (SMS, Voice, WhatsApp) and channel orchestration logic.<br/><br/></p><p>- Familiarity with Docker, Kubernetes, CI/CD, and scalable cloud infrastructure (AWS/GCP/Azure).</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations


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