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About The Role We are seeking an AI Engineer to design and build production-ready LLM applications, including Retrieval-Augmented Generation (RAG) pipelines and multi-agent AI systems. The role focuses on strong system design, clean architecture, and practical implementation of intelligent, tool-integrated solutions. You will be responsible for end-to-end development from document indexing and semantic retrieval to agent orchestration and context management ensuring scalable, accurate, and well-documented AI systems. Responsibilities Design and implement end-to-end RAG pipelines including document processing, vector indexing, and semantic retrieval Integrate LLMs to generate accurate, context-grounded responses Develop and orchestrate multi-agent AI systems with structured handoffs Implement tool integrations for FAQs, seat updates, and external workflows Manage per-user session context and state across agent interactions Write clean, modular, and well-documented code Test, debug, and optimize system performance and retrieval accuracy Document architecture decisions, assumptions, and trade-offs Collaborate with stakeholders to refine requirements and improve solutions Requirements Experience building LLM-powered applications and RAG systems Strong understanding of embeddings, vector databases, and semantic search Hands-on experience with frameworks such as LangChain, LangGraph, or similar Proficiency in Python and building RESTful APIs Experience integrating external tools and APIs within AI workflows Solid understanding of system design, microservices, and scalable architectures Ability to manage session state and context in conversational systems Strong debugging, problem-solving, and optimization skills Clear communication skills with the ability to explain technical decisions and trade-offs Nice to Have Experience with agent orchestration frameworks and multi-agent architectures Hands-on experience with FAISS, Chroma, Pinecone, or Azure Cognitive Search Experience deploying AI systems using Docker and Kubernetes Familiarity with cloud platforms such as AWS, Azure, or GCP Experience with evaluation frameworks for LLM performance and retrieval quality Knowledge of prompt engineering and guardrail implementation techniques Experience building production-grade chatbots or conversational AI systems Understanding of CI/CD pipelines and DevOps best practices What We Offer Opportunity to work on cutting-edge Generative AI and agentic systems High ownership and autonomy in technical decision-making Exposure to real-world, production-grade AI implementations Collaborative and innovation-driven work environment Flexible work arrangements Competitive compensation and growth opportunities Opportunity to shape AI architecture and best practices from the ground up Continuous learning and skill development in emerging AI technologies .
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.