Backend AI/ML Engineer(Strong Python)

3 days ago

Indore, Madhya Pradesh, India Ccube Full time ₹ 12,00,000 - ₹ 36,00,000 per year


Position: Backend AI/ML Engineer(Strong Python)
Location- Indore, MP (Hybrid , 3 Days a week)
Experience: 3–8 years 
Full-time 

  About the Role: This role transcends traditional backend development. We're seeking a highly skilled Backend AI/ML Engineer with strong Python expertise and a working understanding of Full Stack systems. You'll architect and scale backend infrastructures that power our AI-driven products, while also collaborating across frontend, blockchain, and data science layers to deliver end-to-end, production-grade solutions. You will engineer the backbone for advanced AI ecosystems — building robust RAG pipelines, autonomous AI agents, and intelligent, integrated workflows. Your work will bridge the gap between foundational ML models and scalable, high-performance applications.  

 Key Responsibilities 

 Architect & Build Scalable AI Systems 

 ● Design, develop, and deploy high-performance, asynchronous APIs using Python and FastAPI.
 ● Ensure scalability, security, and maintainability of backend systems powering AI workflows. 

 Develop Advanced LLM Workflows 
 ● Build and manage 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
 ● Architect full Retrieval-Augmented Generation (RAG) systems — including data ingestion, embedding creation, and semantic search across vector databases such as Pinecone, Qdrant, or Milvus. 

 Design and Deploy AI Agents 
 ● Construct autonomous 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. 

 Workflow Automation & Integration 
 ● Implement system and process automation using n8n (preferred) or similar platforms.
 ● Integrate core AI services with frontend, blockchain, or third-party APIs through event-driven architectures. 

 Full Stack Collaboration (Good to Have) 
 ● Contribute to frontend integration and ensure smooth communication between backend microservices and UI layers. 
 ● Understanding of React, , or TypeScript is a plus.
 ● Collaborate closely with full stack and blockchain teams to align AI services with user-facing applications.

 Optimize & Deploy ML Models 
 ● Serve and maintain a variety of ML models in production. 
 ● Implement robust monitoring, logging, and testing practices for AI-driven systems.   

  Required Skills & Qualifications 
 ● Expert-level 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. 
 ● Production-level RAG implementation experience.
 ● Experience integrating ML models with backend APIs.
 ● Strong understanding of containerization (Docker, Kubernetes) and CI/CD workflows. 
 ● Excellent problem-solving, architecture, and debugging skills  

  Preferred / Good-to-Have: 
 ● Frontend Familiarity: Basic to intermediate knowledge of or similar frameworks for integration testing and full-stack alignment. 
 ● Workflow Automation: Experience with n8n, Airflow, or equivalent orchestration tools. 
 ● Blockchain Awareness: Understanding of blockchain integration with AI/ML workflows is a strong plus. (At CCube, Blockchain = Full Stack + AI — cross-functional collaboration is highly valued.) 
 ● Broad ML Knowledge: Familiarity with classical ML models (SVM, GBM, Clustering) and deep learning architectures (CNNs, RNNs, Transformers). 
 ● Protocol Design: Experience defining custom communication protocols (e.g., MCP – Model Context Protocol).
 ● DevOps/MLOps: Hands-on with AWS / GCP / Azure, pipelines, and model deployment tools. 
 ● Data Engineering Basics: Exposure to ETL pipelines, Kafka/RabbitMQ, or streaming architectures

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