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Job Description: We are seeking a highly skilled Machine Learning Engineer to join our team. The ideal candidate will have a strong background in Natural Language Processing (NLP), Large Language Models (LLMs), and Python programming. You will work closely with data scientists, product managers, and data engineers to design, develop, and deploy high-performance AI/ML models and integrate generative AI solutions into existing workflows. Your responsibilities will include: Collaborating with cross-functional teams to design and deliver high-performance AI models, including NLP, computer vision, semantics engines, linguistic analysis, risk management, and time-series prediction models. Integrating generative AI solutions into existing workflow systems.Developing and maintaining the ML Operations CI/CD pipeline for seamless deployment and monitoring. Training, tuning, and optimizing AI models and algorithms for enhanced performance.Implementing complex real-time data and AI/ML applications to capture knowledge and automate decision-making processes.Creating ML/AI models for business teams and establishing metrics to track their accuracy and performance. Overseeing the full lifecycle of algorithm development, from ideation to deployment and monitoring. Evaluating and ranking ML algorithms based on their potential success in solving specific problems.Serving as an internal resource for AI/ML needs, providing guidance and insights to stakeholders during strategic discussions. Required Experience and Skills: Machine Learning: Proficient in generative AI techniques, prompt engineering, and Retrieval-Augmented Generation (RAG) (3+ years).Experience with Large Language Models (LLMs) such as OpenAI, Gemini, LLAMA, and other state-of-the-art models (3+ years).Expertise in using ML/AI libraries such as Pandas, NumPy, PyTorch, TensorFlow, Keras, BERT, LayoutLM, and traditional ML algorithms (5+ years).Experience with distributed ML/AI training libraries/models: Koalas, Horovod, DDP. Python Programming and Software Engineering: Expertise in Pythonic clean coding practices, including the use of decorators, generators, and descriptors (5+ years).Strong understanding of software design principles such as DRY, OAOO, YAGNI, KIS, EAFP/LBYL, and defensive programming (2+ years).Proficient in software design concepts focusing on cohesion and coupling (2+ years). Knowledge of SOLID principles (2+ years). Education and Experience: Minimum Bachelor's degree or foreign equivalent in Computer Science, Electrical Engineering, or a closely related field.At least 5 years of experience as a software engineer and 5 years of ML-related programming. .
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.