Backend ai/ml engineer

5 days ago

Varanasi, India TalentLynk Full time

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 Fast API. 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 Fast API, 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

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