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 creative 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

📌 Solution Engineer II (AI/ML lead) (India)
🏢 Arrow Electronics
📍 India