Software Engineer Future Opportunities

As an Software Engineer – Agentic AI , you will be a hands-on builder contributing to the development of production-grade agentic AI systems that securely operate on enterprise and cloud data while serving both internal and customer-facing use cases. You should be experienced and responsible for software design, development, testing, and implementation. Experience must include 1 year of experience with: Any of these - Agile, algorithms, API, API Rest, GIT, J2EE, Java, JavaScript, JDBC, Jenkins, JSON, JUnit, Linux, Log4J, Maven, object-oriented programming, performance tuning, RDBMS, relational databases, REST, Spring, Spring framework, Spring MVC, SQL, Unix, Waterfall, and XML You will work alongside experienced engineers, cloud architects, security engineers, product managers, and designers to design, build, and deploy AI-powered capabilities that automate cloud security, risk management, governance, and operational workflows. This role provides opportunities to grow technical expertise in LLMs, agentic systems, cloud security, and production AI engineering while operating within a highly regulated enterprise environment. This is not a research-only role. You will write production-quality code, contribute to architecture and security design discussions, implement AI-powered cloud security solutions, and support the systems you build throughout their operational lifecycle. What You'll Do Design and implement LLM-powered and agentic AI solutions that automate cloud security, governance, and operational workflows. Build and extend agentic AI workflows capable of reasoning over enterprise security context, invoking cloud security tools, and performing automated actions with appropriate guardrails and human oversight. Develop AI agents that integrate with enterprise cloud platforms, SaaS applications, identity services, CNAPP, CSPM, SSPM, SIEM, and security orchestration platforms. Design and maintain Retrieval-Augmented Generation (RAG) pipelines using enterprise security documentation, cloud architecture, policies, incident data, and operational knowledge while ensuring correctness, privacy, and regulatory compliance. Develop Security-as-Code and Policy-as-Code capabilities that enable AI agents to validate cloud configurations, enforce security guardrails, and recommend or automate remediation activities. Contribute to shared AI infrastructure including LLM services, orchestration frameworks, vector databases, agent evaluation frameworks, and AI observability tooling. Build AI-powered automation for cloud security assessments, threat modeling, security posture analysis, compliance validation, and risk reporting. Participate in operating AI systems in production, including monitoring model performance, evaluating agent behavior, debugging issues, improving reliability, and ensuring secure AI operations. Collaborate with cloud engineering, cybersecurity, platform engineering, product, and business teams to translate security and operational requirements into scalable AI-enabled solutions. Ensure AI systems are designed with enterprise security, governance, explainability, auditability, and responsible AI principles.