Data Architect

Project Role : Data Architect
Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.
Must have skills : Edge Computing
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
As an Edge Computing and Computer Vision Architect/Manager, you will own the architecture and technical delivery of complex Edge AI solutions involving cameras, sensors, AI/ML models, edge compute platforms and cloud systems. You will lead technology and hardware selection, solution architecture, performance optimization and large-scale deployment while providing technical leadership to engineering teams and engaging with client and product stakeholders.

Roles & Responsibilities:
Architect end-to-end Edge AI and Computer Vision solutions covering cameras, sensors, edge compute, AI inference, applications, actuators and cloud platforms.
Define edge architecture and select appropriate hardware based on compute, Graphics Processing Unit (GPU)/Neural Processing Unit (NPU) capability, power, thermal, environmental, connectivity and Total Cost of Ownership (TCO) requirements.
Evaluate platforms such as NVIDIA Jetson, industrial x86 PCs, embedded System-on-Chip (SoC) platforms and dedicated AI accelerators.
Define camera architecture involving RGB (Red-Green-Blue), thermal/infrared, low-light, stereo, depth, Time-of-Flight (ToF) and specialized imaging technologies.
Architect sensor and actuator integration involving LiDAR (Light Detection and Ranging), radar, ultrasonic sensors, Inertial Measurement Units (IMUs), Programmable Logic Controllers (PLCs), motors, relays and industrial equipment.
Lead selection and design of Computer Vision solutions for object detection, segmentation, tracking, classification, Optical Character Recognition (OCR), pose estimation, anomaly detection, activity recognition and depth estimation.
Evaluate model architectures including YOLO, SSD, Faster R-CNN, Mask R-CNN, DETR, U-Net, Vision Transformers (ViTs) and emerging Vision-Language Models (VLMs).
Define model optimization strategies including quantization, pruning, knowledge distillation, FP16/INT8 inference and hardware-specific acceleration.
Architect high-performance video and inference pipelines using OpenCV, GStreamer, NVIDIA TensorRT, NVIDIA DeepStream, Intel OpenVINO, ONNX Runtime or equivalent platforms.
Define camera interface and interoperability strategies using USB, MIPI-CSI (Mobile Industry Processor Interface – Camera Serial Interface), GigE Vision, RTSP (Real-Time Streaming Protocol), ONVIF and other relevant protocols.
Define requirements for camera calibration, multi-camera synchronization, sensor fusion and image/video preprocessing.
Architect edge solutions supporting local/offline operation, intermittent connectivity, data residency and edge-to-cloud synchronization.
Define secure edge architectures including secure boot, device identity, certificate management, encrypted communication, access control and protection of AI models and Intellectual Property (IP).
Define device lifecycle capabilities including provisioning, fleet management, Over-the-Air (OTA) software and model updates, remote monitoring, observability, health management and rollback.
Lead architecture and technical delivery from Proof of Concept (PoC) through pilot and large-scale production rollout.
Design solutions capable of supporting hundreds or thousands of cameras and edge devices across distributed locations.
Define technical Key Performance Indicators (KPIs) covering accuracy, latency, Frames Per Second (FPS), throughput, reliability, bandwidth, power and operational performance.
Lead architecture reviews, design reviews, technology evaluations and technical risk assessments.
Provide technical leadership, mentoring and guidance to engineering teams.
Work with product management, business teams and clients to translate business requirements into scalable technology solutions and roadmaps.

Professional & Technical Skills:
Must To Have Skills: Strong architecture and hands-on expertise in Edge Computing, Computer Vision and Machine Learning.
Strong knowledge of edge hardware architectures covering Central Processing Unit (CPU), GPU, NPU and dedicated AI accelerator technologies.
Deep understanding of camera technologies, optics, image sensors, resolution, frame rate, Field of View (FoV), thermal imaging, low-light performance and camera selection.
Strong knowledge of modern Computer Vision model architectures and their suitability for different use cases.
Experience with PyTorch, TensorFlow and Open Neural Network Exchange (ONNX) ecosystems.
Strong understanding of model optimization and inference acceleration on resource-constrained edge platforms.
Experience with Python, C/C++, Linux, Docker and containerized edge applications.
Strong understanding of video processing, camera pipelines and real-time inference architectures.
Experience with edge-to-cloud architectures, MQTT (Message Queuing Telemetry Transport), REST APIs and Internet of Things (IoT) platforms.
Understanding of Machine Learning Operations (MLOps), dataset lifecycle, annotation, model versioning, deployment, monitoring and model drift.
Strong knowledge of edge security, remote device management and fleet lifecycle management.
Experience designing for real-world environmental challenges such as lighting variations, weather, vibration, motion blur, occlusion and camera movement.
Ability to make architecture trade-offs across accuracy, latency, hardware cost, power, bandwidth, reliability, scalability and Total Cost of Ownership.
Strong client-facing communication, technical leadership and solution consulting skills.

Additional Information:
The candidate should have minimum 12 years of overall technology experience with strong experience in Edge Computing, Computer Vision and AI/ML solutions.
Candidate should have demonstrated experience architecting and delivering Computer Vision solutions from Proof of Concept through large-scale production deployment.
Strong preference for candidates who have experience with deployments involving hundreds or thousands of cameras, sensors or distributed edge devices.
Candidate should have experience making hardware, camera, AI model and platform architecture decisions rather than focusing only on AI model development.
Experience in industrial, manufacturing, transportation, automotive, retail, robotics, surveillance or smart infrastructure domains will be preferred.
Experience leading engineering teams and interacting with senior client stakeholders is expected.
This position is based at our Bengaluru office.
A 15 years full time education is required.


15 years full time education

About Accenture

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