AI Engineer

Role Overview

Build and maintain scalable backend services and design APIs that expose AI/LLM-powered capabilities to web, mobile, and enterprise applications. You will be responsible for building RAG pipelines, agentic workflows, and AI-enabled automation flows while handling LLM operational concerns like prompt management and cost optimization.

Responsibilities

• Build and maintain scalable backend services using modern technologies such as Python, Node.js, Java, or Go.
• Design and implement APIs that expose AI/LLM-powered capabilities to web, mobile, and enterprise applications.
• Integrate production-grade LLM APIs (OpenAI, Anthropic, Gemini, Azure OpenAI, etc.) into backend systems and workflows.
• Build and manage Retrieval-Augmented Generation (RAG) pipelines including ingestion, chunking, embedding, indexing, retrieval, reranking, and grounding.
• Build agentic workflows and multi-step reasoning systems using frameworks such as LangGraph, CrewAI, AutoGen, or equivalent.
• Design AI-enabled automation flows using tools such as n8n, Temporal, Airflow, or similar orchestration platforms.
• Work with vector databases and search platforms such as Pinecone, Weaviate, Qdrant, Elasticsearch, or FAISS.
• Implement secure tool integrations and MCP (Model Context Protocol)-based workflows.
• Design monitoring and evaluation pipelines for AI systems including tracing, prompt/version tracking, and hallucination analysis.

Requirements

• 4–7 years of backend engineering experience with strong system design fundamentals.
• Hands-on experience building AI-powered backend systems, RAG pipelines, or agentic workflows in production environments.
• Strong understanding of LLM limitations, hallucination mitigation, grounding strategies, and cost-performance tradeoffs.
• Experience designing scalable AI infrastructure and enterprise-grade APIs.
• Strong debugging, performance optimization, and problem-solving skills.

Skills

• Python
• Node.js
• LangGraph
• Pinecone
• AWS