Research Engineer (Chatbot)

Conversational AI / Multi-Turn Dialog Systems Pocket FM is building the world's most advanced AI copilot for fiction writers, helping them go from a blank page to a fully produced audio series across web and mobile. We build AI that understands narrative structure, character arcs, pacing, emotional continuity, and story evolution across hundreds of episodes, not a generic chatbot. With 100M+ listeners globally, we sit at the intersection of generative AI, storytelling, and entertainment infrastructure. Role Overview We are looking for a Research Engineer with hands-on experience in designing, building, and deploying AI conversational chatbots and multi-turn dialog agents end-to-end. The ideal candidate should have strong expertise in LLMs, NLP, dialog orchestration, retrieval systems, memory handling, evaluation frameworks, and production deployment of conversational AI applications across any domain (customer support, healthcare, fintech, gaming, education, enterprise AI, etc.) This role combines applied research with engineering execution to build scalable, intelligent, and context-aware conversational systems. Key Responsibilities: Conversational AI Development Design and develop multi-turn conversational agents using LLMs and dialog management frameworks. Build end-to-end chatbot systems including: Intent understanding Context management Conversation memory Tool/function calling Response generation Conversation orchestration Implement intelligent workflows using: Agentic AI architectures Retrieval-Augmented Generation (RAG) Knowledge-grounded conversations Hybrid search systems Develop domain-adaptive conversational experiences across structured and unstructured data sources. Applied AI & Research Experiment with state-of-the-art LLMs, open-source models, and agent frameworks. Fine-tune or optimize models for conversational quality, latency, and cost efficiency. Research improvements in: Multi-agent systems Long-context memory Persona consistency Hallucination reduction Dialogue evaluation Reasoning workflows Prototype and benchmark new conversational AI capabilities. System Engineering Build scalable APIs and backend systems for conversational applications. Integrate AI agents with enterprise systems, databases, vector stores, and external tools. Design conversation pipelines using frameworks such as: LangChain LangGraph LlamaIndex Semantic Kernel Rasa Haystack Custom orchestration frameworks Work with vector databases such as Pinecone, Weaviate, FAISS, Milvus, or ChromaDB. Optimize inference pipelines for production deployment. Evaluation & Monitoring Develop automated evaluation systems for chatbot quality and user experience. Measure: Conversation success rate Hallucination frequency Retrieval accuracy User satisfaction Latency and reliability Implement observability and analytics for conversational systems. Required Qualifications: Technical Skills Strong programming skills in: Python (preferred) JavaScript/TypeScript Backend development frameworks Experience with: OpenAI APIs / Anthropic / Gemini / open-source LLMs Prompt engineering Function calling / tool usage RAG pipelines Multi-turn dialogue systems Agent orchestration Hands-on experience with: Vector databases Embedding models NLP pipelines Conversational memory architectures Experience: 2+ / 4+ / 6+ years (adjustable based on role level) in AI/ML or conversational AI engineering. Proven experience building and deploying AI chatbots end-to-end in production environments. Experience working on: Customer support bots Voice assistants AI copilots Enterprise AI assistants Workflow automation agents Domain-specific conversational systems Preferred Qualifications: Experience with fine-tuning LLMs or training conversational models. Familiarity with speech systems: ASR TTS Voice agents Knowledge of reinforcement learning, ranking systems, or conversational UX. Experience deploying AI systems on cloud platforms: AWS GCP Azure Understanding of AI safety, guardrails, and responsible AI systems. What We're Looking For: Strong problem-solving and research mindset. Ability to rapidly prototype and iterate on AI products. Passion for conversational AI and agentic systems. Ownership mentality with production-focused engineering skills. Ability to work cross-functionally with product, design, and ML teams. Example Tech Stack Python, FastAPI, Node.js OpenAI, Anthropic, Hugging Face LangChain, LangGraph, LlamaIndex Pinecone, Weaviate, FAISS Postgres, Redis Docker, Kubernetes AWS/GCP/Azure