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Backend AI/ML Engineer (with Full Stack Expertise)
Backend AI/ML Engineer (with Full Stack Expertise)Confidential • Indore, India
Backend AI / ML Engineer (with Full Stack Expertise)

Backend AI / ML Engineer (with Full Stack Expertise)

Confidential • Indore, India
18 days ago
Job description

Position : Backend AI / ML Engineer (with Full Stack Expertise)

Experience : 3–8 years Type : Full-time | Hybrid 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, Next.js, 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 React.js 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.
  • Skills Required

    Docker, FastAPI, react.js , Kubernetes, Python

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