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We are seeking an experienced AI Engineer to design and build production-grade Generative AI solutions with a strong focus on agentic workflows, multi-agent systems, and enterprise AI applications. The ideal candidate combines strong software engineering fundamentals with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks. Responsibilities Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot Studio, or similar technologies Develop and maintain backend services, APIs, microservices, and data pipelines that power AI-driven products Implement advanced AI patterns including RAG, Agentic RAG, tool/function calling, planning & reflection loops, and human-in-the-loop workflows Engineer and optimize prompts, system instructions, and agent workflows to improve reliability, accuracy, and user experience Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories Monitor, evaluate, and continuously improve model and agent performance using observability tools, metrics, and user feedback Collaborate closely with Product, Engineering, Data, and Design teams to deliver impactful AI solutions Stay current with emerging AI technologies, frameworks, and best practices, contributing innovative ideas to the team Document architectures, design decisions, and reusable solution patterns while supporting knowledge sharing across teams Requirements 57 years of professional experience in software development Hands-on experience building applications using Generative AI and LLM technologies Strong proficiency in Python and experience developing production-ready applications Hands-on experience with at least two agentic AI frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot extensibility Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI/ADK Strong backend development skills, including REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask Solid understanding of leading LLMs including OpenAI GPT models, Anthropic Claude, Google Gemini, and open-source alternatives Practical experience building RAG solutions using vector databases such as Pinecone, Weaviate, ChromaDB, or Qdrant Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation techniques Strong problem-solving, system design, and architectural decision-making skills Excellent communication skills with the ability to collaborate effectively across global teams .
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.