ML Research Engineer – Agentic AI & LLMs

About the role

Clarrio AI is seeking a Research Engineer with strong applied AI research capabilities to build next‑generation conversational and agentic AI systems. You will work on AI copilots, autonomous workflows, and advanced LLM pipelines, turning prototypes into production‑ready features.

Key responsibilities

• Design and develop AI‑powered chatbots, copilots, and autonomous agents.
• Build and optimise LLM pipelines using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Experiment with agentic AI architectures, including planning, memory, tool usage, orchestration, and multi‑agent systems.
• Fine‑tune prompts, retrieval systems, and conversation flows to improve reliability and user experience.
• Develop scalable Retrieval‑Augmented Generation (RAG) systems connected to structured and unstructured data.
• Collaborate with product, engineering, and design teams to define intelligent user experiences.
• Evaluate model performance and iterate using metrics, experimentation, and user feedback.
• Stay updated with the latest advancements in GenAI, LLMs, open‑source models, and AI infrastructure.

Required profile

• Curious and highly experimental mindset.
• Strong problem‑solving abilities and comfort with ambiguity.
• Passion for emerging AI systems and product‑oriented engineering.
• Ability to move quickly from prototype to production.

Required skills

• 3+ years of software engineering, machine learning, or AI engineering experience.
• Strong Python development skills.
• Hands‑on experience building applications with LLMs or generative AI.
• Experience with APIs and AI model providers such as OpenAI, Anthropic, Gemini, Hugging Face, or open‑source models.
• Familiarity with conversational AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, etc.).
• Knowledge of vector databases and embeddings (Pinecone, Weaviate, Chroma, FAISS) and RAG architectures.
• Understanding of prompt engineering and AI evaluation methodologies.
• Experience with FastAPI, Docker, and cloud infrastructure (AWS, Azure, GCP).