ML/AI Engineer

You are a ML/AI Engineer with the opportunity to join a team working on an innovative AI/ML platform focused on structured financial conversations. Your role involves designing, building, and deploying intelligent agents and machine learning solutions to enhance the platform. **Responsibilities:** - Design, develop, and implement AI agents using modern multi-agent frameworks like CrewAI, LangChain, LangGraph, or similar. - Build and deploy Natural Language Processing (NLP) models and Large Language Models (LLMs) for tasks such as Q&A, summarization, and topic prediction. - Develop Retrieval Augmented Generation (RAG) pipelines for knowledge retrieval, reasoning, and explainability. - Implement model evaluation and monitoring strategies to ensure reliability and accuracy of LLM-based systems. - Build and maintain scalable data processing and normalization pipelines. - Collaborate with frontend and backend engineers to seamlessly integrate AI services into the product. - Contribute to observability and monitoring efforts using tools like Prometheus, Jaeger, Kibana, or equivalent. - Participate in code reviews, testing, and technical documentation. **Required Skills:** - Strong programming skills in Python. - Practical experience with Large Language Models (LLMs), including prompt engineering and fine-tuning. - Experience with multi-agent systems and orchestration frameworks like CrewAI, LangChain, LangGraph, or similar. - Knowledge of Retrieval Augmented Generation (RAG) using tools such as LlamaIndex, LangChain, etc. - Hands-on experience in Machine Learning (ML) and Natural Language Processing (NLP). - Proficiency with data processing libraries like Pandas, NumPy. - Understanding of API development and integration. - Basic knowledge of cloud platforms and deployment concepts. - Experience with monitoring and observability tools like Prometheus, Jaeger, Kibana. - Ability to work collaboratively in a cross-functional team. **Preferred / Good-to-Have Skills:** - Experience with MLOps practices for deploying, monitoring, and scaling models. - Familiarity with financial domain applications or conversational AI systems. - Exposure to time-series forecasting methods.,