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Want to work with cutting-edge technologies on long-term, strategic projects that combine Deep Learning, Computer Vision, Edge AI, and IoT ? 🚀🤖🚜 At Marvik, we are leading the next evolution of smart machinery—integrating vision capabilities, real-time image processing, and multi-sensor fusion into physical products operating in real environments. 🌟 What do we offer?: Challenging, real-world projects: Work on stuff other people only read about—bringing AI out of the cloud and straight into physical, intelligent machinery. State-of-the-art tech stack: Optimize and deploy cutting-edge Computer Vision and Deep Learning models on Edge devices. Strategic growth: Join an expanding team with huge potential for long-term ownership, leadership, and professional development. Great team culture: Excellent work environment full of highly motivated, collaborative, and talented professionals who elevate each other. Flexible work style: Opportunity to work remotely with global, high-impact clients. 🧑🏻💻 Responsibilities: Take end-to-end ownership of Machine Learning models, moving them beyond training and ensuring reliable deployment in production on physical hardware. Design, build, and optimize real-time AI pipelines, integrating foundation models, sensor and application data, and scalable inference workflows. Optimize inference performance, memory usage, and execution speed for Edge AI platforms (e.g., NVIDIA Jetson, embedded platforms). Collaborate directly with clients and cross-functional engineering teams, building trust, proposing proactive solutions, and maintaining smooth technical communication. Debug, monitor, and maintain production models operating continuously under real-world hardware constraints. 🤝 If you have: Strong experience bringing AI to production: It’s not just about training models—you know how to make them run reliably in real-world environments. Solid background in AI & Deep Learning, including experience with LLMs, Computer Vision, multimodal models, model optimization, and efficient inference. Edge AI & Embedded exposure: Experience optimizing models for constrained devices (TensorRT, ONNX, OpenCV, C++, or Python). Strong Ownership & Soft Skills: Highly collaborative, proactive, independent, and clear in technical communication. A team player who builds trust with clients and peers. Advanced English level: Excellent verbal and written communication skills to interact directly with international client teams. Required tools: Python, C++, PyTorch/TensorFlow, OpenCV, Docker, Git. 🦾 It’s a major plus: Hands-on experience with NVIDIA Jetson, ROS/ROS2, or embedded hardware platforms. Experience in domains such as Robotics, IoT, Drones, Automotive, or Industrial Machinery. Knowledge of sensor fusion (IMU, cameras, LiDAR) or OTA (Over-The-Air) updates and Cloud IoT architectures (AWS/Azure IoT).
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.