About the Client
ARAs Client is a cutting-edge technology organization building secure, scalable AI systems for enterprise environments. The company focuses on integrating Generative AI with cybersecurity principles, ensuring AI applications are robust, compliant, and production ready.
Role Summary
We are looking for a Lead AI Cybersecurity Engineer who combines expertise in LLM-based systems and Agentic AI with a strong understanding of secure system design.
This role goes beyond building AI systems—you will ensure they are secure, reliable, and resilient against vulnerabilities, while designing advanced multi-agent architectures for real-world applications.
Key Responsibilities
- Lead the design, development, and deployment of secure AI applications using prebuilt generative models
- Architect LLM-based systems with security-first principles
- Build multi-agent and single-agent systems with:
- Planning
- Tool usage
- Memory
- Reflection
- Define agent architectures using:
- LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom stacks
- Integrate LLMs with APIs, databases, and external tools securely
- Identify and mitigate risks such as:
- Prompt injection
- Data leakage
- Model misuse
- Optimize AI systems for performance, scalability, and security compliance
- Collaborate with cross-functional teams to define AI use cases
- Mentor engineers on secure AI development practices
- Communicate technical and security concepts to stakeholders
Must-Have Qualifications
- 8+ years of software development experience
- 5+ years in AI/ML with applied, production-grade systems
- Strong experience building LLM applications beyond chatbots
- Proven expertise in Agentic AI systems
- Strong understanding of:
- Planning, reasoning, tool use, memory, reflection
- Proficiency in Python and/or TypeScript
- Hands-on experience with:
- LangGraph, AutoGen, CrewAI, Semantic Kernel
- Experience implementing:
- ReAct, tool-calling agents, hierarchical planning
- Experience integrating:
- LLMs with APIs, databases, external tools
- Strong experience with:
- RAG pipelines, LangChain, LlamaIndex, RAGAS
- Experience designing:
- Short-term and long-term memory systems
- Experience deploying AI systems to production
- Strong system design and debugging skills
- Ability to lead architecture decisions
