Principal AI Architect for Edge-------Remote

Position: AI Architect for Edge Computing

Location: Frisco TX - Remote - Travel

As a lead AI Architect for Edge Computing, you will be responsible for designing, developing, and deploying advanced solutions with AI capabilities for edge devices. You will work on integrating AI-driven solutions directly into the edge devices to enhance real-time data processing, reduce latency, and enable efficient decision-making. You will collaborate with cross-functional teams to build scalable and resilient architectures that can handle large volumes of data generated at the edge.

Key Responsibilities:

1. Design and Architecture leadership:
• Lead the design and evolve scalable high-performance architectures for edge computing AI/ML environments.
• Define the strategy for data processing, storage, and AI model deployment at the edge.
• Create high-level and low-level architectural designs for edge devices and their integration into IoT ecosystems.
• Evaluate emerging technologies (e.g., edge accelerators, federated learning, TinyML, 5G) and recommend adoption strategies.
2. AI & Machine Learning Integration:
• Lead the development and deployment of AI models (e.g., deep learning, reinforcement learning) for real-time analytics at the edge.
• Collaborate with AI researchers and data scientists to optimize models for low-power, low-latency environments.
• Ensure AI models run efficiently on edge devices, optimizing for resource constraints (memory, processing power, and battery life).
3. Edge Computing Strategy:
• Build a robust edge computing strategy, including architecture for local data processing, storage, and analytics.
• Lead the design of distributed systems that balance data processing between edge devices and cloud environments.
• Define edge-to-cloud communication protocols and ensure data synchronization and consistency.
4. Collaboration & Documentation:
• Provide GTM support to Business development and sales teams
• Collaborate with product teams, data engineers, and software developers to implement end-to-end AI/ML solutions.
• Define architecture designs, AI/ML model deployments, and integration procedures for internal stakeholders.
• Provide technical leadership and mentorship to junior engineers in the team.
5. Security and Compliance:
• Ensure secure AI systems for edge computing environments, ensuring data privacy and protection.
• Ensure regulatory and compliance standards for data transmission and storage on edge devices especially in sensitive industries like healthcare, manufacturing, or automotive.
6. Continuous Learning and Improvement:
• Stay up-to-date with the latest trends in IoT, edge computing, and AI/ML.
• Research and adopt emerging technologies to improve system performance, scalability, and energy efficiency.

Technical Skills for AI Architect for Edge

1. IoT Technologies:
• IoT Protocols: MQTT, CoAP, HTTP/HTTPS, AMQP, DDS, and LWM2M.
• Edge Computing Platforms: AWS Greengrass, Azure IoT Edge, Google Cloud IoT, NVIDIA Jetson, Raspberry Pi, and other edge computing hardware and software.
• IoT Security: Secure communication, device authentication, and encryption techniques (TLS/SSL, OAuth).
2. AI and Machine Learning:
• Machine Learning Frameworks: TensorFlow Lite, PyTorch Mobile, Keras, Scikit-learn, ONNX .
• Deep Learning: CNN, RNN, LSTM, Reinforcement Learning for edge devices.
• Edge AI: Deployment of machine learning models on edge devices with low computational power and limited storage.
• AI Optimization: Techniques like model pruning, quantization, and hardware accelerators for efficient edge AI execution.
3. Edge Computing and Distributed Systems:
• Edge Computing Architecture: Design patterns for edge networks, data aggregation, and processing. Familiarity with hardware accelerators (TPUs, GPUs, FPGAs) and embedded systems.
• Containerization: Docker, Kubernetes for edge device orchestration.
• Fog Computing: Knowledge of fog computing layers and the relationship with edge/cloud environments.
• Data Processing Frameworks: Apache Kafka, Apache Flink, Apache Spark (for distributed data processing on the edge).
• Edge operations software platforms : Dell Native Edge / equivalent
4. Cloud Integration:
• Cloud Platforms: AWS, Azure, Google Cloud, and other platforms for IoT and edge computing.
• Cloud-Edge Integration: Syncing data between cloud and edge devices, designing hybrid systems for cloud/edge workflows.
• API Development: RESTful API design, gRPC, and WebSocket for communication between devices, edge, and cloud.
5. Networking and Communication:
• Networking Protocols: TCP/IP, UDP, Bluetooth, Zigbee, LoRaWAN, NB-IoT, and 5G networks.
• Low Power Communication: LPWAN (Low Power Wide Area Network) technologies like LoRa, NB-IoT.
• Network Security: IPsec, VPNs, firewalls, and other techniques for secure communications between edge devices.
6. DevOps and Automation:
• CI/CD: Continuous Integration and Continuous Delivery practices for edge device software.
• Automation Tools: Jenkins, GitLab, Ansible, and other automation tools for edge deployment and management.
7. Software Development:
• Programming Languages: Python, C/C++, Java, JavaScript (for edge apps), and embedded systems programming.
• Embedded Systems Development: Experience with microcontrollers, RTOS, embedded Linux (Yocto, OpenWRT).
8. Data Management:
• Databases: NoSQL databases (MongoDB, Cassandra) and SQL databases optimized for edge computing.
• Data Pipelines: ETL pipelines for processing IoT data and pushing it to cloud or local storage.
9. Problem-Solving and Performance Optimization:
• Performance Optimization: Techniques for minimizing latency, reducing energy consumption, and optimizing throughput on resource-constrained edge devices.
• Fault Tolerance and Reliability: Designing systems that are resilient to edge device failures and network outages.
10. Soft Skills:

• Leadership: Strong leadership and communication skills to guide cross-functional teams and interact with stakeholders.
• Analytical Thinking: Ability to analyze complex problems and create innovative solutions.