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The AI Engineer develops, tests, integrates, and deploys AI solutions to meet business requirements. Working under an AI Lead, the role covers data preparation, AI model development, Generative AI using Large Language Models (LLMs), enterprise application integration, and deployment across AWS, GCP, and on-premises environments. Key Responsibilities AI Model Development: Develop, evaluate, and fine-tune AI models for Natural Language Processing, Computer Vision, and Audio Processing. Generative AI and LLM Solutions: Develop applications using LLMs, prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and AI agents. Data Preparation: Cleanse, label, transform, and integrate structured and unstructured data, including text, documents, images, and audio. AI Service Development: Build and maintain AI services and REST APIs using Python frameworks such as FastAPI. System Integration: Integrate AI services with enterprise applications and support functional, performance, and integration testing. Cloud and On-Premises Deployment: Support the deployment and operation of AI applications across public cloud platforms, such as AWS and GCP, as well as local/on-premises infrastructure using Docker, Linux, and GPU-enabled servers. CI/CD and MLOps: Familiarity with basic CI/CD and MLOps concepts, including automated testing, application packaging, deployment, monitoring, and release processes. Experience with Git-based platforms such as GitLab or GitHub is an advantage. Technical Research: Research and evaluate emerging AI technologies, frameworks, and tools through documentation, technical papers, and proof-of-concept testing. Collaboration and Documentation: Work with Product Owners, System Analysts, Developers, DevOps Engineers, and Cloud Engineers to deliver solutions and maintain technical documentation. Qualifications 3 years of experience in software development, AI applications, or related technical projects. Strong proficiency in Python and familiarity with software development best practices. Good understanding of AI, Deep Learning concepts, model evaluation, and frameworks such as PyTorch or TensorFlow. Basic practical experience or strong interest in LLMs, prompt engineering, RAG, vector databases, and AI agents. Familiarity with Natural Language Processing, Computer Vision, or Audio Processing. Familiarity with Azure AI services or other cloud platforms (AWS/GCP) Excellent problem-solving abilities and a continuous learning mindset. Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, Information Technology, or a related field. Location: Kongboonma, Silom Working hours: Mon – Fri, 8:30 AM– 17:30 PM
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.