About this role
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