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Job Description: We are looking for a senior Applied AI/ML Engineer with strong hands-on experience in Computer Vision and video systems to help lead the development of production-grade AI solutions for video understanding, detection/segmentation, tracking, multimodal reasoning and model-assisted annotation. This is a practical research-and-engineering role: the person should be comfortable taking a problem from data and experimentation through model training, evaluation, optimization and deployment. Prior Physical AI or robotics experience is not mandatory, but the candidate must have enough depth in vision/video ML to grow into those areas. What you will own Own or co-own the technical design of applied AI/ML solutions with a strong Computer Vision and video component, from problem definition and data strategy through deployment. Build, fine-tune and benchmark models for areas such as object detection, instance/semantic segmentation, tracking, video understanding, action recognition or other temporal vision tasks. Design reliable datasets, annotation schemas, gold sets and leakage-safe evaluation protocols, and perform systematic failure analysis across real-world video conditions. Evaluate modern vision, video and multimodal models and decide when to use a general-purpose model, a specialist model, or a hybrid pipeline. Optimize GPU inference and integrate versioned models into production services, working with backend/MLOps and annotation-platform teams. Profile: We are open to experienced Applied AI/ML candidates whose strongest work is in Computer Vision, video ML or multimodal systems. The exact previous job title is not important; demonstrated end-to-end ownership of real model-development work is. Relevant sourcing profiles / titles include: Senior Applied AI/ML Engineer; Senior AI/ML Engineer Vision & Multimodal Systems; Senior Computer Vision Engineer; Machine Learning Engineer Computer Vision; Video AI Engineer; Applied Scientist Computer Vision; Multimodal AI Engineer; and Applied ML Research Engineer. Typically 48+ years of relevant hands-on experience in applied AI/ML, with a meaningful portion of that work involving Computer Vision or video. Strong candidates with fewer years but clear end-to-end model ownership may also be considered. B.Tech / M.Tech / MS / PhD in Computer Science, AI, Data Science, Electrical/ECE, Mathematics & Computing, Engineering Physics, Robotics, Mechatronics or a related technical discipline. Equivalent demonstrated industry or research depth is also acceptable. Must Have: Strong Python and hands-on PyTorch experience, including model training, fine-tuning, debugging and experiment design. Solid practical experience in Computer Vision with real model-development work across at least two of the following: object detection, segmentation, tracking, pose estimation, video understanding, action recognition/localization or related temporal vision problems. Hands-on experience processing real image/video data using OpenCV, FFmpeg or equivalent tooling, including frame/clip preparation, augmentation and data-quality handling. Ability to design datasets and evaluation protocols correctly: train/validation/test construction, leakage prevention, class imbalance, hard negatives, gold-set creation and reproducible benchmarking. Strong model-evaluation and failure-analysis skills; able to reason about errors caused by motion blur, occlusion, domain shift, lighting, viewpoint changes, tracking drift or poor segmentation boundaries. Experience training or fine-tuning deep-learning models on GPUs, with practical understanding of VRAM, batching, mixed precision, throughput and inference trade-offs. Experience taking models beyond notebooks: Docker-based deployment and API/model serving using FastAPI or equivalent, with practical cloud/GPU deployment exposure. Ability to compare multiple technical approaches rather than simply integrate a single popular model, and to explain architecture decisions using measurable quality, latency and reliability trade-offs. Ability to provide technical guidance to junior engineers while remaining hands-on with code, experiments and debugging. Good to Have: Multimodal/VLM or open-vocabulary vision experience, including vision-language grounding, video-language understanding or multimodal retrieval. Egocentric/first-person video, Physical/Embodied AI, hand-object interaction, manipulation data or annotation-automation experience. RGB-D/depth, 3D vision, camera calibration, SLAM/VIO, pose estimation, reconstruction or robotics perception. Experience with modern model families or equivalent approaches such as Grounding DINO, SAM-style models, Florence, Qwen-VL, InternVideo, ActionFormer, Mask2Former or contemporary detection/segmentation/video architectures. ONNX, TensorRT, Triton Inference Server, CUDA/performance profiling, distributed training or multi-GPU .
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.