Overview

Position: Solution Engineer II (AI/ML lead)

Responsibilities

• 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.
• 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
• 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
• 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.

Qualifications

• B.Tech/M.Tech or Ph.D. in Computer Science, Electronics, or related engineering domain.
• Typically requires 8–12 years of equivalent
• 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.