Backend AI / ML Engineer (with Full Stack Expertise)

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

Experience : 3–8

Type : Fulltime| 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

Design, develop, and deploy high-performance, asynchronous APIs using Python and FastAPI.

Ensure scalability, security, and maintainability of backend systems powering AI 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.

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

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.

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.

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