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We are seeking an innovative and results-driven AI Engineer to design, develop, and deploy artificial intelligence, machine learning, Generative AI, and full-stack solutions that solve real business challenges. The ideal candidate will have experience building AI-powered applications, training and integrating machine learning models, developing LLM and Generative AI solutions, building scalable web applications, and deploying enterprise solutions on cloud platforms. The candidate should have strong hands-on experience in Python, AI/ML frameworks, Generative AI, LLMs, RAG, React, Node.js, NestJS/Express, MongoDB, APIs, databases, and cloud technologies, along with the ability to collaborate with cross-functional teams to deliver scalable, secure, and production-ready solutions. Key Responsibilities Design, develop, and implement AI/ML models and intelligent applications. Build and optimize machine learning pipelines for training, testing, deployment, and monitoring. Develop and integrate Generative AI, Large Language Models (LLMs), AI-powered copilots, and intelligent assistants. Fine-tune, evaluate, and monitor AI models to ensure accuracy, reliability, performance, and cost efficiency. Implement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, semantic search, and vector database solutions. Develop and integrate AI solutions with enterprise applications through REST APIs and third-party services. Design and develop responsive front-end applications using React.js, JavaScript, and TypeScript. Develop scalable backend services and APIs using Node.js, Express.js, and NestJS. Design and integrate databases including MongoDB, SQL, PostgreSQL, and other NoSQL databases. Build end-to-end AI-powered applications by integrating AI models, APIs, frontend applications, backend services, and databases. Deploy AI and full-stack applications using cloud platforms such as Microsoft Azure, AWS, or Google Cloud. Work with cloud-based AI services, compute, storage, databases, networking, and security services. Implement containerized application deployments using Docker and Kubernetes. Build and maintain CI/CD pipelines for AI and application deployment. Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management. Ensure AI solutions comply with security, privacy, governance, and responsible AI standards. Monitor production systems, troubleshoot issues, and continuously improve application and model performance. Collaborate with business stakeholders, product managers, UI/UX designers, and engineering teams to identify AI use cases and deliver business solutions. Prepare technical designs, prototypes, proof-of-concepts, and architecture for AI-powered applications. Stay current with advancements in AI, machine learning, Generative AI, cloud technologies, and modern software engineering practices. Required Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. 1+ years of experience in AI/ML development, software engineering, Generative AI, or a related field. Strong programming skills in Python and experience with AI frameworks such as TensorFlow, PyTorch, or Scikit-learn. Strong knowledge of Generative AI technologies, LLMs, prompt engineering, RAG architectures, embeddings, and vector search. Good programming experience in JavaScript/TypeScript. Hands-on experience with React.js for developing modern web applications. Hands-on experience with Node.js and backend frameworks such as Express.js or NestJS. Experience working with APIs, databases, and cloud platforms. Experience with MongoDB and/or SQL-based databases. Understanding of MLOps practices, model deployment, monitoring, and CI/CD. Understanding of Docker and containerized application deployment. Strong analytical, problem-solving, debugging, and communication skills. Ability to work collaboratively with product, business, engineering, and DevOps teams. Preferred Qualifications Experience with Azure AI Services, Azure OpenAI Service, Microsoft Copilot Studio, or Microsoft Fabric. Hands-on expertise with vector databases such as Pinecone, Weaviate, Azure AI Search, or similar technologies. Experience with LangChain, AI Agents, and Agentic AI architectures. Experience building enterprise AI copilots, intelligent .
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.