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 systemsyou 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 :

1. Planning

2. Tool usage

3. Memory

4. Reflection

- Define agent architectures using :

1. LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom stacks

- Integrate LLMs with APIs, databases, and external tools securely

- Identify and mitigate risks such as :

1. Prompt injection

2. Data leakage

3. 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 :

1. Planning, reasoning, tool use, memory, reflection

- Proficiency in Python and/or TypeScript

- Hands-on experience with :

1. LangGraph, AutoGen, CrewAI, Semantic Kernel

- Experience implementing :

1. ReAct, tool-calling agents, hierarchical planning

- Experience integrating :

1. LLMs with APIs, databases, external tools

- Strong experience with :

1. RAG pipelines, LangChain, LlamaIndex, RAGAS

- Experience designing :

1. Short-term and long-term memory systems

- Experience deploying AI systems to production

- Strong system design and debugging skills

- Ability to lead architecture decisions