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The ideal candidate will have strong hands-on experience building enterprise GenAI applications, including LLM-powered solutions, RAG architectures, AI Agents, and real-time AI systems. The candidate should have solid software engineering fundamentals with the ability to take GenAI solutions from architecture to production. Key Responsibilities & Skills - Design, develop, and deploy production-grade Generative AI applications using LLMs and foundation models including OpenAI (GPT/Codex), Azure OpenAI, Anthropic Claude, Google Gemini, Meta Llama, and Mistral. - Build scalable GenAI solutions including RAG pipelines, AI Agents, multi-agent workflows, enterprise copilots, and intelligent automation platforms using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or CrewAI. - Strong hands-on experience with prompt engineering, context engineering, structured outputs, function/tool calling, LLM orchestration, and GenAI application optimization. - Experience building enterprise GenAI applications using Python, APIs, microservices, vector databases (Pinecone, Weaviate, Milvus, FAISS, Azure AI Search, Elasticsearch/OpenSearch), and cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI). - Experience with modern AI engineering tools and workflows including Cursor, GitHub Copilot, and AI-assisted development practices. - Strong understanding of production GenAI challenges including scalability, latency, cost optimization, evaluation, monitoring, security, hallucination mitigation, and responsible AI. - Ability to work directly with clients, present technical solutions, collaborate with stakeholders, and mentor engineering teams. Experience Requirements - 6 to 12 years of overall software engineering experience. - Strong hands-on experience building and deploying production-grade GenAI/LLM applications. - Strong programming experience in Python and modern application development. - Experience owning GenAI solutions from design through production deployment. - Excellent problem-solving, communication, client-facing, and presentation skills. .
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.