Backend AI/ML Engineer (with Full Stack Expertise)

Indore, Madhya Pradesh, India
Full Time
Mid Level
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.

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