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Position: Senior AI Engineer Vision Language Models (VLM) Experience: 4+ Years Relevant Experience: 2+ Years in Multimodal AI / VLM Employment Type: Contractual Work Mode: Remote Notice Period: Immediate Joiners / Short Notice Preferred About the Role We are looking for a Senior AI Engineer Vision Language Models (VLM) with strong hands-on experience in Multimodal AI, Computer Vision, Generative AI, and Video Analytics . The selected candidate will design, develop, optimize, and deploy AI solutions focused on video understanding, scene analysis, activity recognition, and real-time incident detection . The role requires experience taking AI solutions from research and prototyping through production deployment , with strong expertise in VLMs, deep learning, Python, PyTorch, and GPU-based inference. Key Responsibilities VLM & Multimodal AI Design and develop Vision Language Model (VLM) solutions for video understanding and incident detection. Build multimodal AI systems combining visual, textual, and contextual information. Work with modern multimodal foundation models such as GPT-4o Vision, Gemini, Qwen-VL, LLaVA, InternVL, Florence , or equivalent. Develop prompting, reasoning, and evaluation strategies for complex visual scenarios. Improve model performance through experimentation, prompt engineering, feedback loops, and iterative optimization. Video Understanding & Incident Detection Develop AI pipelines for real-time or near-real-time video analysis . Build solutions for detecting incidents such as: Falls Physical restraints Altercations Unsafe or unusual activities Other safety-related incidents Work on scene understanding, activity recognition, action recognition, temporal reasoning, and video analytics . Process and analyze large-scale image and video datasets. Model Evaluation & Optimization Benchmark VLMs against traditional Computer Vision and Action Recognition models . Define appropriate evaluation methodologies and performance metrics. Measure and optimize accuracy, precision, recall, F1 score, latency, and inference performance . Conduct experiments to identify the most effective models and approaches for specific use cases. Production Deployment Deploy and optimize AI models across cloud and/or edge GPU infrastructure . Build efficient inference pipelines for real-time video processing. Optimize models for GPU utilization, latency, scalability, and resource efficiency . Containerize AI applications using Docker. Apply MLOps best practices for model deployment and lifecycle management. Troubleshoot AI models and production inference pipelines. Collaboration Collaborate with Data Scientists, ML Engineers, Software Engineers, and Product teams . Translate business requirements into scalable AI solutions. Contribute to technical architecture, design, testing, documentation, and optimization. Take solutions from POC/research stage to production . Mandatory Technical Skills AI / ML Strong proficiency in Python . Hands-on experience with PyTorch . Strong understanding of: Computer Vision Deep Learning Generative AI Multimodal AI Vision Language Models Vision Language Models Hands-on experience with one or more of: GPT-4o Vision Google Gemini Qwen-VL LLaVA InternVL Florence Equivalent VLM / Multimodal Foundation Models Computer Vision & Video Analytics Video analytics Scene understanding Activity recognition Action recognition Image/video processing Temporal reasoning Large-scale image and video datasets Tools & Platforms Hugging Face OpenCV Docker REST APIs Azure AI/ML GPU-based model deployment MLOps GPU & Model Deployment GPU optimization and efficient model inference. Experience deploying AI/ML models on cloud or edge GPU infrastructure . Understanding of inference latency, scalability, resource utilization, and model op .
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.