Job Description
<p><p><b>Role Overview : </b><br/><br/> We are seeking a hands-on AI/ML Engineer with proven expertise in designing, building, and deploying intelligent chatbots.<br/><br/> The ideal candidate will not only be technically strong but also capable of leading a small team of junior engineers, setting technical direction, and ensuring high-quality Responsibilities : </b></p><br/> - Architect, design, and implement AI-driven chatbot solutions using state-of-the-art frameworks (e.g., Rasa, Dialogflow, LangChain, LLM APIs).<br/><br/> - Apply Natural Language Processing (NLP) and Machine Learning techniques for intent classification, entity recognition, and conversation management.<br/><br/> - Lead end-to-end chatbot development lifecycle from data preparation and model training to deployment and monitoring.<br/><br/> - Guide junior engineers through code reviews, mentorship, and technical leadership.<br/><br/> - Collaborate with product, UX, and business teams to ensure chatbot solutions meet user and business requirements.<br/><br/> - Continuously evaluate and integrate new tools, frameworks, and AI advancements (e.g., LLM fine-tuning, vector databases, prompt engineering).<br/><br/> - Ensure production-grade performance, scalability, and security of deployed Skills & Experience : </b></p><br/> - 610 years of professional experience in AI/ML and software engineering, with at least 3+ years building chatbot solutions.<br/><br/> - Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, spaCy, Hugging Face, etc.).<br/><br/> - Strong expertise in NLP techniques, conversational AI frameworks (Rasa, Dialogflow, Botpress, LangChain, etc.), and LLM APIs (OpenAI, Anthropic, etc.).<br/><br/> - Experience with cloud deployment (AWS/Azure/GCP) and containerization (Docker/Kubernetes).<br/><br/> - Knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus) for retrieval-augmented generation (RAG).<br/><br/> - Hands-on experience integrating chatbots with enterprise systems, APIs, and messaging platforms (Teams, Slack, WhatsApp, Web, etc.).<br/><br/> - Strong understanding of ML lifecycle management (MLOps), model deployment, monitoring, and retraining.<br/><br/> - Demonstrated ability to lead small technical teams and deliver projects in Agile : </b></p><br/> - Experience in Generative AI (LLMs, fine-tuning, prompt engineering).<br/><br/> - Background in analytics, recommendation systems, or voice-enabled bots.<br/><br/> - Open-source contributions or published work in chatbot/AI.</p><br/></p> (ref:hirist.tech)