Job Description
Job Description: Principal Accountabilities Collaborate with teams to translate business requirements into technical specifications, system architecture, and ML pipelines. Drive end-to-end solution delivery — including data preparation, model development, optimization, validation, deployment, and continuous improvement. Provide technical guidance and mentorship to junior engineers and data scientists; review and refine their designs and code implementations. Develop reusable ML frameworks, model training workflows, and inference pipelines for rapid prototyping and deployment. Evaluate and integrate state-of-the-art AI/ML technologies to continuously improve model efficiency and system design. Respond to client RFQs and provide robust technical proposals and solution architectures. Partner cross-functionally with system engineers, embedded developers, and application teams for integrated AI system delivery. Job Complexity & Impact Demonstrates expert-level depth across machine learning, system integration, and model optimization. Mentors ML teams with minimal supervision. Defines best practices for AI model lifecycle management and process improvements. Solves complex problems by combining innovative and existing methods to deliver production-grade AI solutions. Represents the level at which career may stabilize for many years or even until retirement Work Responsibilities Mentor 2–5 member AI engineering team for full-cycle ML product development. Architect, implement, and optimize AI models for edge computing platforms ensuring high throughput, accuracy and low latency. Develop and benchmark AI model pipelines on NVIDIA Jetson (Nano & Xavier), Qualcomm Snapdragon 835 and i.MX8 platforms or any other constrained platform. To work on platforms like Snapdragon Neural Processing Engine (SNPE), FastCV, Halide, Deep stream etc. as per requirement. Collaborate closely with embedded and application teams to ensure successful AI system integration Key Technical Competencies Deep Learning Frameworks: TensorFlow, PyTorch, ONNX, Keras, Caffe and TensorRT Computer Vision & Perception: Object detection, instance segmentation, depth estimation, pose estimation, activity recognition, image super-resolution, GANs. ML System Architecture: Designing scalable ML pipelines for training, validation, and inference on edge and cloud Hardware Acceleration & Optimization: CUDA, TensorRT, OpenCL and DeepStream. Edge & Embedded Platforms: NVIDIA Jetson (Nano/Xavier/Orin), Qualcomm Snapdragon, NXP i.MX8, Google Coral, Raspberry Pi Programming Expertise: Python, C++, Java (optional: Rust, Go) Data & Model Pipelines: Docker, Kubernetes for ML orchestration Deployment & Serving: Flask/FastAPI/Django for REST APIs, ONNX Runtime MLOps: CI/CD integration for ML (Git, Jenkins, Docker), versioning, reproducibility, and model governance Cloud AI Services: AWS Sagemaker, Azure ML (good to have) Familiarity with NVIDIA RTX and DGX platforms for training large models.
