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AI/ML Engineer (4+ Years) Were building AI systems that convert unstructured visual data into structured, usable outputs that power real-world workflows. This role focuses on solving messy, high-variability problems and delivering reliable systems in production, not just training models in isolation. What Youll Do - Build and improve end-to-end AI/ML pipelines (input preprocessing inference structured output) - Work with noisy, inconsistent datasets and drive improvements in data quality and model performance - Develop preprocessing pipelines (e.g., normalization, transformations, input standardization) - Run experiments, tune models, and evaluate trade-offs across accuracy, latency, and cost - Collaborate with engineering teams to productionize models (APIs, batch/real-time systems) What Were Looking For - ~4 years of hands-on experience in ML/AI with exposure to computer vision - Strong Python skills with production-quality coding practices - Solid understanding of microservices and system design. - Experience building and deploying ML systems beyond experimentation - Experience working on systems that extract or process information from complex visual inputs (e.g., documents, forms, or similar structured outputs) - Strong understanding of neural network architectures (e.g., CNNs, Transformers, sequence models) and their real-world trade-offs. - Ability to: - Select appropriate approaches based on problem constraints - Fine-tune and optimize models for performance and reliability - Debug model behavior and failure cases in production - Strong understanding of evaluation metrics, error analysis, and model limitations Valuable to Have - Experience with cloud environments (e.g., Azure or similar) - Familiarity with scalable data pipelines, containerization, or distributed systems - Exposure to multi-modal systems (vision + text) What We Dont Want - Only academic or demo project experience - Over-reliance on plug-and-play APIs without understanding internals - Inability to explain past work end-to-end (data model output impact) - Profiles focused only on generic image classification or object detection use cases without real-world deployment experience Compensation: 5,062.93 - 1,500,000.00 per year Benefits: - Paid time off - Provident Fund Work Location: In person .
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.