About the role
Responsibilities:
- Contribute to the design and development of agentic AI frameworks for proactive threat detection and response.
- Implement multi-agent coordination logic for planning, execution, and self-correction in dynamic security workflows.
- Integrate LLMs, vector databases, and tool-use APIs to enable contextual memory and autonomous reasoning.
- Build modular orchestration components for knowledge routing, task delegation, and agent collaboration.
- Collaborate with cross-functional teams to ensure production-grade scalability, observability, and resilience.
- Prototype and evaluate emerging agentic AI tools (e. g., LangChain, Semantic Kernel, LlamaIndex, AutoGen, CrewAI).
- Participate in code reviews, design discussions, and contribute to technical documentation.
Requirements:
- Exposure to cybersecurity, threat detection, or SOC operations.
- Experience with vector databases (e. g., FAISS, Pinecone) and retrieval-augmented generation (RAG).
- Familiarity with ML Ops tools and practices (e. g., MLflow, Kubeflow).
- Contributions to open-source AI or automation projects.
- 3+ years of experience in backend software development, distributed systems, or applied AI engineering.
- Solid experience with Python, Go, or similar backend programming languages.
- Familiarity with LLMs and agentic AI frameworks such as LangChain, Semantic Kernel, or OpenAI/Anthropic APIs.
- Understanding of cloud-native architectures and microservices (AWS, GCP, or Azure).
- Passion for building intelligent systems that can reason, learn, and act autonomously.
- Strong problem-solving skills and a collaborative mindset.
- Experience working in agile, cross-functional teams.