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We are looking for a hands-on AI Engineer to help build our foundational AI infrastructure from the ground up. The ideal candidate will have strong experience in Python, LangChain, Agentic AI, RAG, vector databases, and LLM applications. You will design, develop, evaluate, and deploy intelligent applications with a strong focus on Agentic AI and Retrieval-Augmented Generation (RAG) systems. Key Responsibilities Design and develop production-ready RAG architectures and Agentic AI workflows. Build robust data ingestion and processing pipelines for unstructured and semi-structured data. Work with web crawlers, OCR technologies, document parsing, and Docling to extract and structure information. Develop LLM-powered applications using Python and LangChain. Build and integrate AI agent tools using Model Context Protocol (MCP). Implement vector databases, embeddings, semantic search, and retrieval pipelines. Evaluate and benchmark LLM/RAG applications using Ragas and other evaluation techniques. Perform prompt engineering, response evaluation, and optimization to improve accuracy and relevance. Design scalable and maintainable AI solutions suitable for production environments. Collaborate with engineering teams to define AI architecture, development standards, and best practices. Contribute to technical decisions in a greenfield AI environment and help establish the organization's AI technology foundation. Required Skills Strong proficiency in Python. Hands-on experience with LangChain. Robust experience building Agentic AI / AI Agent systems. Deep understanding of RAG architecture and LLM application development. Experience with Vector Databases, embeddings, semantic search, and retrieval techniques. Experience with Ragas or LLM evaluation frameworks. Strong knowledge of Prompt Engineering. Hands-on experience with OCR, document parsing, Docling, and web crawling. Understanding of MCP (Model Context Protocol) and agent tool integration. Experience working with LLMs, APIs, and AI application development. Nice to Have Experience deploying ML/AI models into production. Experience with AWS, GCP, or Azure. Knowledge of Docker, Kubernetes, CI/CD, and MLOps. Experience with additional LLM frameworks such as LlamaIndex, LangGraph, or Hugging Face. Experience working with databases such as PostgreSQL, MongoDB, or vector databases. Key Skills Python | LangChain | Agentic AI | RAG | Vector Database | LLM | Ragas | MCP | Prompt Engineering | OCR | Docling | Web Crawling Skills: prompt,vector,ocr,python .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.