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Industrial AI, OCR, and edge deployment, within the Product Engineering team. Chennai (On-site, with client-site travel) Full-time 6 to 10 years Reports ToProduct Engineering Lead About LIFO Technologies LIFO Technologies is a Chennai-based deep-tech company building products and engineering solutions across computer vision, embedded systems, RFID, industrial IoT, and power electronics. We work with enterprise and industrial customers, with production AI and vision systems running across customer sites under real-world lighting, environmental, and operational constraints. The Role We are looking for a senior hands-on computer vision engineer to take technical ownership of vision capability within our Product Engineering team. Reporting to the Product Engineering Lead, you will partner with hardware, firmware, platform, and field teams to deliver vision modules that ship and run reliably at customer sites. This is not a pure research role. It is best suited to an engineer who enjoys moving between model development, edge deployment, site conditions, and team execution. What You Will Own Technical Delivery End-to-end delivery of vision modules within product engineering programs, from problem definition to production rollout. Model selection, training, evaluation, optimisation, deployment, and post-deployment improvement loops. Build and improve systems across OCR, barcode and QR, object detection, tracking, anomaly detection, and industrial video analytics. Optimise models for edge and on-prem deployments using NVIDIA Jetson, TensorRT, ONNX Runtime, and GPU servers. Work with field and hardware teams on camera placement, calibration, lighting, data capture, and deployment reliability. Review code, raise engineering quality, mentor junior engineers and annotation teams, and create reusable pipelines, deployment playbooks, and evaluation standards. Solutioning Support Support the Product Engineering Lead and sales team in technical discovery with prospect engineering, operations, and plant teams. Convert ambiguous customer asks into workable solution architectures, effort estimates, and POC plans. Define camera coverage, edge hardware, inference flow, integration approach, and dashboard requirements for new opportunities. Contribute technical sections to proposals, pilot scopes, and customer presentations. Must Have 6 to 10 years hands-on experience in computer vision, deep learning, or applied ML, with at least 2 years leading customer-facing deployments. Strong Python and software engineering habits: version control, code review, testing, reproducibility, and maintainable production code. Deep experience with PyTorch or TensorFlow, and solid command of OpenCV-based video and image pipelines. Proven experience shipping vision systems from prototype to production, not only training offline models. Experience across OCR, object detection, classification, segmentation, industrial code reading, or video analytics and tracking. Hands-on experience with edge or on-prem inference stacks: NVIDIA Jetson, TensorRT, ONNX Runtime, or similar. Experience handling real-world camera and deployment constraints: low light, motion blur, occlusion, camera angle, dust, glare, and unstable field environments. Ability to integrate vision outputs into APIs, dashboards, and operational systems. Comfort working with engineers and business stakeholders. Willingness to travel to customer sites when required. Nice to Have MLOps or experiment tracking tools such as MLflow, DVC, or Weights and Biases. Prior work in industrial environments with dust, glare, rain, low light, and camera inconsistency. Experience in manufacturing, utilities, energy, transportation, mobility, or large multi-site operations. Experience with multi-camera systems, evidence workflows, or video systems that stand up in audit or operational review. Exposure to embedded Linux, camera integration, or hardware-aware optimisation. Why Join LIFO Work on deployed systems alongside hardware, firmware, and platform engineers. Broad, commercially relevant problem set: OCR, industrial analytics, safety, monitoring, industrial vision, and edge AI. Direct collaboration with the product engineering team, not behind a handoff layer. Help shape team standards, productisation patterns, and the reusable solution stack as the company scales. .
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.