Designing and architecting innovative AI and automation capabilities for business applications that leverage artificial intelligence, intelligent automation, data platforms, analytics and any other technologies which comprise the Embedded Intelligence technology stack.
Requirements
- Strong programming skills in multiple languages (e.g., Python, Go, TypeScript/JavaScript)
- Hands-on experience with DevOps practices including Kubernetes, CI/CD pipelines, containerization, and orchestration technologies
- Experience with cloud platforms (AWS, Azure, GCP) and cloud-native infrastructure patterns
- Experience with AI-assisted development tools and modern development workflows
- Understanding of software architecture principles, microservices, and API design patterns
- Demonstrated experience in solution architecture for AI/ML applications or systems design
- Experience with AI/ML evaluation and monitoring tools (e.g., LangFuse, Arize, or similar observability platforms)
Responsibilities
- Design and architect core AI capability frameworks including AI Ops, Model Context Protocol (MCP), Agent2Agent Protocol (A2A), Agent Registry, RAG (Retrieval-Augmented Generation) systems, and intelligent search capabilities
- Develop comprehensive technical architectures for machine learning, computer vision, digital twin, and embedded intelligence applications with consideration for enterprise integration
- Evaluate and recommend AI/ML technologies including large language models, computer vision tools, and intelligent automation platforms
- Establish observability and evaluation frameworks for AI applications, including performance monitoring, quality assessment, and continuous improvement processes
- Create architectural blueprints that ensure scalability, maintainability, security, and integration with existing enterprise systems
- Design permissioning and security frameworks for AI applications, including OAuth/Okta integration and row-level security (RLS) implementations
- Create cloud-native, containerized solutions using Kubernetes, CI/CD pipelines, and modern DevOps practices to enable rapid prototyping and scalable deployment
Other
- Bachelor's degree or equivalent experience
- 6+ years of experience in software engineering, solution architecture, or related technical roles with focus on AI/ML systems
- Clear communication skills with ability to explain complex AI/ML concepts to diverse audiences
- Self-directed with ability to work with minimal supervision in R&D environment
- Collaborative approach with strong teamwork orientation and mentoring capabilities
