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Job Description: We are seeking an experienced engineer who brings two distinct skill sets: AI/ML (Computer Vision) - owning the training, testing, and tuning of vision-based models for live camera-feed monitoring; and Native Android Development - developing and shipping a production Android app that talks to our cloud. You will partner with the deployment team to align models with their scaling/runtime constraints, work with the maintenance team to maintain and upgrade models. Key Responsibilities Design, train, evaluate, and tune computer vision models (detection, classification, segmentation, tracking) for live video and multi-camera use cases, including dataset curation, training/validation/testing pipelines, and rigorous benchmarking on accuracy, latency, and throughput. Optimize models (architecture, quantization, pruning, distillation) to meet deployment and scaling constraints provided by the deployment team; re-tune or re-architect when those constraints change. Partner with the operations team to maintain and upgrade models in production triage regressions, refresh on new data, address drift, and ship improved versions. Design, build, and ship a native Android application (Kotlin, Jetpack Compose, MVVM/Clean Architecture, Hilt, Coroutines/Flow, Room, WorkManager) that interacts with our cloud backend. Build secure cloud integration: REST/gRPC, OAuth2/JWT, TLS, FCM push, and offline-first sync; handle Android runtime permissions and background execution correctly. Set up Android CI, testing (unit + instrumentation), crash reporting, and Play Store release pipelines. Required Qualifications 5+ years of total professional software engineering experience. Proven experience in vision-based model training and testing, with models shipped to production. Hands-on experience with cloud-based AI training/experimentation infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML, or equivalent). Experience building models for continuous monitoring via live video input from cameras and tuning them for efficient inference across multiple concurrent camera streams. Track record of collaborating with deployment/MLOps/operations teams - translating runtime constraints into model decisions and supporting models post-launch. Proficiency in Python and ML frameworks (PyTorch, TensorFlow, TensorFlow Lite, ONNX); strong grasp of CNNs and modern detection/tracking architectures (YOLO, DETR, ByteTrack, etc.). Strong proficiency in Kotlin and the modern Android stack (Jetpack, Compose, Coroutines/Flow, Hilt, Room, WorkManager); demonstrated experience shipping production Android apps to the Play Store. Deep working knowledge of Android Wi-Fi and BLE APIs. Experience with cloud connectivity on Android (REST, gRPC, WebSockets, or MQTT; OAuth2/JWT; TLS; FCM) and Androids security/permissions model (runtime permissions, foreground services, background execution limits). Comfort interfacing with embedded/IoT hardware over BLE and WiFi under real-world conditions (intermittent connectivity, retries, power constraints). Preferred Qualifications On-device inference acceleration (Android NNAPI, Qualcomm SNPE, MediaPipe, GPU delegates). Streaming protocols (RTSP, WebRTC, HLS) and video codecs; edge / IoT camera deployments. Model monitoring, drift detection, and active learning loops. Contributions to open-source ML or Android projects. Education Bachelors or Masters degree in Computer Science, AI/ML, or related field or equivalent practical experience. measurement bias Strong product mindset and bias for measurement - you instrument, benchmark, and optimize. Excellent collaboration skills with deployment and operations teams, and a maintenance mindset toward models in production. Ability to switch effectively between two distinct domains (ML model development and Android app development) and deliver in both. .
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.