Solution Engineer II (AI/ML lead)

Description

Principal Accountabilities

• Collaborate with teams to translate business requirements into technical specifications, system architecture, and ML pipelines.
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• Drive end-to-end solution delivery — including data preparation, model development, optimization, validation, deployment, and continuous improvement.
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• Provide technical guidance and mentorship to junior engineers and data scientists; review and refine their designs and code implementations.
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• Develop reusable ML frameworks, model training workflows, and inference pipelines for rapid prototyping and deployment.
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• Evaluate and integrate state-of-the-art AI/ML technologies to continuously improve model efficiency and system design.
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• Respond to client RFQs and provide robust technical proposals and solution architectures.
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• Partner cross-functionally with system engineers, embedded developers, and application teams for integrated AI system delivery.
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Job Complexity & Impact

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• Demonstrates expert-level depth across machine learning, system integration, and model optimization.
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• Mentors ML teams with minimal supervision.
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• Defines best practices for AI model lifecycle management and process improvements.
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• Solves complex problems by combining innovative and existing methods to deliver production-grade AI solutions.
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• Represents the level at which career may stabilize for many years or even until retirement
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Work Responsibilities

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• Mentor 2–5 member AI engineering team for full-cycle ML product development.
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• Architect, implement, and optimize AI models for edge computing platforms ensuring high throughput, accuracy and low latency.
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• Develop and benchmark AI model pipelines on NVIDIA Jetson (Nano & Xavier), Qualcomm Snapdragon 835 and i.MX8 platforms or any other constrained platform.
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• To work on platforms like Snapdragon Neural Processing Engine (SNPE), FastCV, Halide, Deep stream etc. as per requirement.
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• Collaborate closely with embedded and application teams to ensure successful AI system integration
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Key Technical Competencies

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• Deep Learning Frameworks: TensorFlow, PyTorch, ONNX, Keras, Caffe and TensorRT
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• Computer Vision & Perception: Object detection, instance segmentation, depth estimation, pose estimation, activity recognition, image super-resolution, GANs.
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• ML System Architecture: Designing scalable ML pipelines for training, validation, and inference on edge and cloud
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• Hardware Acceleration & Optimization: CUDA, TensorRT, OpenCL and DeepStream.
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• Edge & Embedded Platforms: NVIDIA Jetson (Nano/Xavier/Orin), Qualcomm Snapdragon, NXP i.MX8, Google Coral, Raspberry Pi
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• Programming Expertise: Python, C++, Java (optional: Rust, Go)
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• Data & Model Pipelines: Docker, Kubernetes for ML orchestration
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• Deployment & Serving: Flask/FastAPI/Django for REST APIs, ONNX Runtime
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• MLOps: CI/CD integration for ML (Git, Jenkins, Docker), versioning, reproducibility, and model governance
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• Cloud AI Services: AWS Sagemaker, Azure ML (good to have)
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• Familiarity with NVIDIA RTX and DGX platforms for training large models.
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Required Qualifications

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• B.Tech/M.Tech or Ph.D. in Computer Science, Electronics, or related engineering domain.
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• Typically requires 8–12 years of equivalent work experience
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• 3–5 years of experience in machine learning, deep learning, and computer vision
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• Proven track record of designing and deploying ML-based systems from concept to production.
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• Academic publications in computer vision research at top conferences and journals.
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• Excellent communication, problem-solving, and presentation skills.
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Location:

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IN-UP-Noida, India-World Trade Tower (eInfochips)\n
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Time Type:

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Full time\n
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Job Category:

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Engineering Services