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About the role We seek a highly motivated AI Engineer to build and deploy end-to-end AI solutions. This role focuses on implementing advanced RAG pipelines, optimizing vector database integrations, and managing local LLM/SLM execution. You will thrive in a rapid prototyping environment, utilizing "vibe coding" to iteratively develop and deploy high-performance AI applications. Key responsibilities End-to-End AI Deployment: Design, develop, and deploy full-stack AI solutions, ensuring seamless integration between model backends and user-facing applications, including the development of robust APIs and CI/CD pipelines for model deployment. RAG Pipeline Implementation: Build and optimize advanced Retrieval-Augmented Generation (RAG) pipelines, including experimentation with advanced chunking strategies, semantic search optimization, and reranking mechanisms to enhance model accuracy and context-awareness. Vector DB Management: Implement, maintain, and optimize vector databases (e.g., Pinecone, Milvus, Qdrant) for efficient high-speed similarity search, ensuring scalability and low-latency retrieval for production-grade applications. Local LLM Execution: Configure and troubleshoot optimized local deployment of LLMs/SLMs using tools like Ollama or LM Studio, focusing on performance tuning, quantization, and minimizing inference latency in resource-constrained or edge environments. Vibe Coding & Prototyping: Utilize rapid prototyping techniques to iteratively build, test, and refine AI features in a collaborative, fast-paced environment. About you Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related quantitative field with 2+ years of professional experience in AI/ML engineering. Hands-on experience building RAG pipelines and integrating Vector Databases (Pinecone, Milvus, Qdrant). Proficiency in Python and deep experience with AI orchestration frameworks (e.g., LangChain, LlamaIndex) and backend web frameworks like FastAPI to build scalable service layers. Practical knowledge of local AI deployment tools and model optimization techniques, with a strong understanding of inference optimization, quantization, and hardware acceleration. Strong Vibe coding skills with the ability to rapidly iterate on code and prototype solutions under pressure. Familiarity with end-to-end deployment workflows, including containerization (Docker) and basic cloud infrastructure.
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.