Backend AI/ML Engineer

Role Overview: As a Backend AI/ML Engineer with expertise in Python, you will play a crucial role in architecting and scaling backend infrastructures that power AI-driven products at the company. You will collaborate across various layers such as frontend, blockchain, and data science to deliver end-to-end, production-grade solutions. Your work will involve building robust RAG pipelines, autonomous AI agents, and intelligent workflows, bridging the gap between foundational ML models and scalable applications. Key Responsibilities: - Architect and build scalable AI systems by designing, developing, and deploying high-performance, asynchronous APIs using Python and FastAPI. Ensure scalability, security, and maintainability of backend systems. - Develop advanced LLM workflows by building multi-step AI reasoning frameworks using Langchain and Langgraph for stateful, autonomous agents. Implement context management, caching, and orchestration for efficient LLM performance. - Engineer end-to-end RAG pipelines, including data ingestion, embedding creation, and semantic search across vector databases like Pinecone, Qdrant, or Milvus. - Design and deploy AI agents capable of multi-step planning, tool usage, and complex task execution. Collaborate with data scientists to integrate cutting-edge LLMs into real-world applications. - Implement workflow automation & integration using platforms like n8n, and integrate core AI services with frontend, blockchain, or third-party APIs through event-driven architectures. - Optionally, contribute to frontend integration and ensure smooth communication between backend microservices and UI layers. Understanding of React, Next.js, or TypeScript is a plus. Collaborate closely with full stack and blockchain teams for seamless alignment. Qualifications Required: - Expert-level proficiency in Python for scalable backend system development. - Strong experience with FastAPI, async programming, and RESTful microservices. - Deep hands-on experience with Langchain and Langgraph for LLM workflow orchestration. - Proficiency in Vector Databases (Pinecone, Qdrant, Milvus) for semantic search and embeddings. - Experience with production-level RAG implementation and integrating ML models with backend APIs. - Strong understanding of containerization (Docker, Kubernetes) and CI/CD workflows. - Excellent problem-solving, architecture, and debugging skills. Company Additional Details (if applicable): At CCube, Blockchain integration with AI/ML workflows is highly valued, where Blockchain equals Full Stack plus AI. The company encourages cross-functional collaboration and values expertise in various technologies such as React.js, n8n, Airflow, AWS/GCP/Azure, and data engineering basics like ETL pipelines and streaming architectures.,