Software Engineer, AI Security

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