Company Overview:
Qualgo is an R&D center specializing in cybersecurity products and solutions. We are on a mission to build a trusted cyberspace where individuals and businesses can thrive with confidence.
Job Summary:
- As a Senior AI Engineer on our product development team, you will bridge the gap between data science and production software. You will be responsible for taking state-of-the-art NLP/LLM models and deploying them into high-performance, real-time environments.
- Because our product intercepts and analyzes communications, you will face unique engineering challenges around latency, scale, and privacy. You will not just be calling third-party LLM APIs. You will architect how our AI operates safely, securely, and instantly, with a strong emphasis on edge computing and on-device ML to help preserve user privacy.
- This role is ideal for someone who is strong in AI deployment and systems engineering, and who can work closely with Data Scientists to bring advanced models into real-world products.
Key Responsibilities:
- Deploy, optimize, and integrate NLP/LLM models into high-performance, real-time production environments.
- Build and maintain AI inference pipelines for low-latency, privacy-sensitive communication analysis use cases.
- Design and implement AI systems that can run efficiently across cloud, edge, and on-device environments.
- Optimize model performance for production through quantization, pruning, distillation, graph optimization, and runtime tuning.
- Convert, benchmark, and deploy models using production-oriented inference frameworks and toolchains.
- Work closely with Data Scientists to take models from experimentation to robust production deployment.
- Evaluate architecture tradeoffs across latency, privacy, model quality, cost, and hardware constraints.
- Design reliable testing, profiling, monitoring, and observability workflows for AI inference systems.
- Contribute to system design decisions for privacy-preserving AI, hybrid on-device/cloud inference, and model lifecycle management.
- Collaborate with backend, product, and app engineering teams to integrate AI capabilities into end-user experiences.
Qualifications:
- Education: Bachelor's degree/ Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field.
- 5+ years of experience in AI Engineering, Applied Machine Learning, ML Systems, or a similar role.
- Strong programming skills in Python and C++.
- Strong experience deploying machine learning models into production environments.
- Experience with edge computing, embedded AI, or on-device ML.
- Hands-on experience with model optimization techniques such as quantization, pruning, distillation, or hardware-aware optimization.
- Experience with production inference frameworks and runtimes such as ONNX Runtime, TensorRT, TFLite, Core ML, or similar technologies.
- Strong understanding of performance optimization for inference systems, including latency, throughput, memory footprint, and hardware utilization.
- Experience building reliable, scalable AI services or runtime components for production systems.
- Ability to work closely with Data Scientists and software engineers to productionize advanced NLP/LLM models.
Nice to have:
- Experience with platforms such as NVIDIA Jetson, Google Coral, or similar edge AI environments.
- Experience deploying AI models on iOS or Android devices.
- Familiarity with mobile development languages such as Swift, Objective-C, Kotlin, or Java.
- Experience in privacy-sensitive domains such as trust & safety, fraud detection, cybersecurity, or communication intelligence.
- Exposure to multilingual NLP systems or small-model deployment for constrained environments.
What we offer:
- Competitive salary and benefits package.
- Opportunity to work on a product that impacts millions of users.
- A dynamic and supportive work environment.
- Premium health insurance for you and your family.
- Professional growth and development opportunities.
- Annual leave 12-14 days per year + 1 Birthday Leave + 1 X'Mas
- Performance review: once per year
- Internal training/sharing and professional Training courses
- Team building, company trip, year end party, monthly activities,....
- Devices: Macbook and screen (If needed)
- Free tea and coffee
- Comfortable working Area
- Working hour: 9am - 6pm from Monday to Friday
