Saviynt is a leader in identity security, providing an AI-powered identity platform that safeguards digital assets for organizations. The Software Engineer, AI Security will design and implement workflows for AI security products, collaborate on software architecture, and serve as a technical expert while driving continuous improvement in engineering processes.
Responsibilities:
- Design, implement, test and release e2e workflows for our AI security product
- Work across multiple agent platforms like AWS Bedrock, Google AgentSpace, Salesforce AgentForce , building foundational solutions, using cloud, SAAS and AI design patterns and technologies
- Use AI and Agents to secure AI, using CUA agents, various LLM’s , agentic frameworks like ADK, Langchain among others
- Design and develop secure, scalable, multi-tenant software solutions that run seamlessly across major cloud platforms like AWS and Azure
- Act as a technical expert and thought leader, influencing product direction and engineering best practices
- Drive continuous improvement in engineering processes, tooling, and operational reliability
- Collaborate with internal teams to produce software design and architecture
- Test and deploy applications and systems
- Revise, update, refactor and debug code
- Ability to start a program from scratch as well as maintain existing services
- Develop documentation throughout the software development life cycle
- Serve as an expert on applications and provide technical support
Requirements:
- 1-3 years of experience operating at a Staff / Principal engineering level
- Expert-level ability utilizing technologies such as Java, Spring Framework, REST and Microservices
- Familiar with AI tools and curious about MCP, A2A, Agentic frameworks. Have a continuous learning mindset and not hesitate to venture into unchartered territory
- Ability to perform research and go deep into platforms is a strong plus
- Strong Experience as a Java Engineer developing applications based on Security principles, cloud platforms (AWS, Azure, or Google Cloud) and Containerization (Docker, Kubernetes)
- Deep understanding of data structures, algorithms, and design patterns
- Hands on experience with SQL, ElasticSearch, Redis, CI/CD, AWS Glue, Kafka
- Experience in increasing levels of responsibility managing application development, solution architecture, design and delivery, and process improvement
- Experience with unit, functional and system integration testing
- Extensive understanding of working in an agile environment utilizing Scrum and Kanban
- Experience with Git (GitHub/GitLab), automatic deployments, continuous integration
- Hands on experience using IntelliJ or Eclipse/My Eclipse IDE, writing Junit test cases, working with Maven/Ant
- Experience with AI development tools in SDLC such as Amazon Q, Github Copilot, Cursor, and similar productivity assistants
- Familiarity with various architectural patterns (e.g., event-driven, microservices, serverless) and their trade-offs