- Communicate design requirements to engineers and management.
- Take end-to-end ownership and manage a small team. Collaborate with the rest of the engineering team to design and launch new features.
- Design of overall architecture and maintain responsiveness and availability of applications.
- Commitment to continuous improvement and elimination of process inefficiencies.
- Maintain code integrity and organization. Understanding and implementation of security and data protection.
- Should be from a premier institute with a relevant engineering degree.
- Knowledge of software architecture, networking and distributed systems, design patterns, and UNIX/Linux environments.
- Experience with browser-side client technologies including HTML5 CSS, ReactJS/Redux, and experience with server-side client technologies.
- Hands-on experience server frameworks such as Flask, Django (Python), or Spring (Java).
- Experience with test-driven development, continuous integration, and continuous deployment processes.
- At least 2+ experience with programming languages: Python or Java.
- Experience with cloud message APIs and usage of push notifications. Database skills - Relational and NoSQL databases.
- Experience with AWS or other cloud environments (Architecture, concepts, and tools) is a plus.
- Should be familiar with concepts of micro-services architecture.
- Experience with SAML, OAuth, WS FEDERATION is a plus.
- Experience with container platforms like Docker is required. Hands-on experience with Kubernetes is a plus.
- Demonstrated excellent communication skills, both written and verbal.
We are an edge computing startup focused on building a data2deploy pipeline for delivering fast and efficient AI applications for commodity and custom hardware. Our applications cater to a wide variety of markets such as fleet/mobility, automotive, retail, and video intelligence in both retail and enterprise domains. At the core of our applications is the edgesensor AI SDK that is powered by our proprietary inference engine that runs on platforms ranging from low-power ARM devices to x64 servers.
We work on a wide range of problems: (a) auto data labeling, (b) developing CV/ML algorithms using deep learning technique, (c) inference engine for fast inference of deep learning algorithms, (d) cross-platform (CPU and OS) AI applications, (e) application license management (online and offline), (g) health monitoring of applications and dashboard analytics on the cloud.
If this excites you and you are one who thinks critically to raise the bar continuously we welcome you to be part of this exciting journey.
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