Staff Software Engineer

About the Role

Key responsibilities include designing and deploying Edge AI solutions for anomaly detection and predictive maintenance, and optimizing AI/ML models to run on constrained edge devices for real‑time insights. The engineer will manage the entire model lifecycle—training, validation, deployment, monitoring, and retraining—while continuously fine‑tuning models to maintain accuracy as data patterns evolve. Additional duties involve validating third‑party connectors and gateways, integrating IoT devices with cloud platforms such as Azure, AWS, or GCP, and providing technical leadership and mentorship to teams. • Design and deploy Edge AI solutions for anomaly detection and predictive maintenance. • Optimize AI/ML models for real‑time inference on constrained edge devices. • Manage the full model lifecycle: training, validation, deployment, monitoring, and retraining. • Validate third‑party connectors, gateways, and converters for reliable edge deployments. • Integrate IoT devices with cloud platforms (Azure, AWS, GCP) to create secure, scalable pipelines. • Provide technical leadership and mentor teams on Edge AI and IoT best practices. • Utilize containerization/orchestration tools (Docker, Kubernetes) for edge workloads. • Design hybrid cloud‑edge architectures for scalable IoT solutions. • Communicate technical concepts clearly to executive stakeholders.

Key Responsibilities

• ▸edge ai
• ▸model optimization
• ▸iot integration
• ▸container orchestration
• ▸hybrid architecture
• ▸model lifecycle

What You Bring

We are seeking an experienced technology professional with 8–10 years of experience and an engineering background (B.Tech or Masters). The ideal candidate has strong expertise in Edge Computing, Edge AI, IoT, and cloud integration. This role blends hands‑on technical leadership with strategic oversight to deploy AI/ML models at the edge for anomaly detection, predictive maintenance, and scalable analytics. Candidates must have 8–10 years of experience in IoT, Edge Computing, or cloud‑based analytics, and hold an engineering degree in Computer Science, Electronics, Electrical, or a related field. Proven expertise with Edge platforms (e.g., Azure IoT Edge, AWS Greengrass, NVIDIA Jetson) and AI/ML frameworks (TensorFlow Lite, PyTorch Mobile, ONNX Runtime) is required, along with hands‑on experience using IoT protocols like MQTT, CoAP, Modbus, and OPC UA. Demonstrated success in predictive maintenance and anomaly detection projects and familiarity with cloud services such as Azure IoT Hub, AWS IoT Core, and Google Cloud IoT are also essential. Preferred attributes include experience with containerization and orchestration tools (Docker, Kubernetes) for edge workloads, and knowledge of hybrid cloud‑edge architectures that enable scalable IoT deployments. Strong communication skills are needed to translate technical details for executive audiences, and a collaborative mindset is essential for leading teams and driving adoption of edge and cloud solutions. • Continuously fine‑tune models to adapt to evolving data patterns.

Requirements

• ▸edge computing
• ▸iot
• ▸docker
• ▸kubernetes
• ▸tensorflow
• ▸engineering

Work Environment

Office Full-Time