Backend AI/ML Engineer(strong Python)

Backend AI/ML Engineer With Strong Python Expertise

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 front end, 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
• Develop Advanced LLM Workflows
• Engineer End-to-End RAG Pipelines
• Design and Deploy AI Agents
• Workflow Automation & Integration
• Full Stack Collaboration (Good to Have)
• Optimize & Deploy ML Models

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.
• 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.