Solution Engineer II (AI/ML lead)

Position: Solution Engineer II (AI/ML lead) 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. Required Qualifications - B.Tech/M.Tech or Ph.D. in Computer Science, Electronics, or related engineering domain. - Typically requires 8–12 years of equivalent work experience - 3–5 years of experience in machine learning, deep learning, and computer vision - Proven track record of designing and deploying ML-based systems from concept to production. - Academic publications in computer vision research at top conferences and journals. - Excellent communication, problem-solving, and presentation skills. Location: IN-UP-Noida, India-World Trade Tower (eInfochips) Time Type: Full time Job Category: Engineering Services