🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Key Responsibilities: Solution Architecture & Deployment: - Design and deploy secure, scalable GenAI architectures integrated into applications - Build and deploy REST APIs for AI/ML models - Work with Docker, Kubernetes in cloud environments (AWS/Azure/GCP) GenAI & LLM Development: - Fine -tune and optimize LLMs (GPT, VAEs, GANs, transformer -based models) - Implement RAG pipelines, embedding, and prompt engineering techniques - Work with commercial and open -source LLMs (GPT, Claude, LLaMA, Phi) Agentic AI Development: - Build and deploy AI agents using LangChain, LangGraph, CrewAI, Autogen, AgentFlow - Implement multi -agent systems, orchestration, tool integration, and state management - Develop autonomous or semi -autonomous workflows for business use cases MLOps & Optimization: - Set up end -to -end MLOps pipelines (CI/CD, monitoring, retraining) - Optimize performance, scalability, and infrastructure costs - Use tools like Git, Docker, Kubernetes, vector databases Application Development & Data Integration: - Develop APIs using FastAPI / Node.js - Work with React, TypeScript, async patterns, WebSockets/SSE - Handle data integration using REST APIs, SQL, and external systems Cross -Functional Collaboration: - Partner with Engineering, Product, and Data teams - Communicate complex AI concepts clearly to technical and non -technical stakeholders - Stay updated with the latest advancements in GenAI and AI agents Required Skills: - Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain) - Hands -on experience with LLMs, RAG, embedding, and prompt tuning - Experience building AI agents and multi -agent systems - Experience with cloud platforms (AWS/Azure/GCP) and containerization - Strong knowledge of REST APIs and data integration - Experience with FastAPI, Node.js, React, TypeScript - Understanding of MLOps and deployment practices - Strong analytical, problem -solving, and communication skills Preferred: - 4+ years of experience with GenAI/LLMs in production - Experience with agent orchestration frameworks (CrewAI, LangGraph, Autogen) - Exposure to client -facing AI solutions or cross -functional projects - Open -source contributions, research, or AI project portfolio Requirements - Robust proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain) - Hands -on experience with LLMs, RAG, embedding, and prompt tuning - Experience building AI agents and multi -agent systems - Experience with cloud platforms (AWS/Azure/GCP) and containerization - Strong knowledge of REST APIs and data integration - Experience with FastAPI, Node.js, React, TypeScript - Understanding of MLOps and deployment practices - Strong analytical, problem -solving, and communication skills Benefits - Competitive salary .
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.