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Design, develop, and deploy cutting-edge LLMs for diverse NLP applications. Optimize and fine-tune large-scale language models using frameworks like TensorFlow, PyTorch, or JAX. Research and implement advancements in LLMs, including transformer architectures and reinforcement learning techniques. Work closely with data scientists, software engineers, and product managers to integrate LLMs into production systems. Build and maintain scalable machine learning pipelines for training and inference. Ensure LLMs are efficient, high-performing, and scalable for real-world applications. Evaluate AI models using key NLP metrics and refine them based on experimental results. Stay updated with the latest AI research and contribute to open-source projects where relevant. Implement responsible AI principles, including fairness, explainability, and ethical considerations. Document AI model architectures, training methodologies, and deployment strategies. Bachelor's, Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Minimum 3-5 years of experience in machine learning, deep learning, or natural language processing roles. Strong proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, or JAX. Hands-on experience with large-scale language models, including transformers, GPT-based models, and BERT-style architectures. Experience with cloud platforms (AWS, Google Cloud, Azure) and ML model deployment. Knowledge of MLOps practices, including model versioning, monitoring, and automation. Experience with large-scale datasets, data preprocessing, and distributed computing frameworks (e.g., Spark, Ray). Solid understanding of deep learning architectures, optimization techniques, and reinforcement learning. Strong problem-solving skills and ability to conduct independent research. Excellent collaboration and communication skills for cross-functional teamwork. Experience with prompt engineering, fine-tuning LLMs, and retrieval-augmented generation (RAG) is a must. Contributions to AI research publications or open-source projects are highly desirable.
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.