Senior AI Engineer – Cyber Architecture, OT and Engineering
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Job Description
Sr. Agentic AI Engineer focused on agentic AI systems and multi-agent frameworks for cybersecurity tasks. Engage in full spectrum of development from architecture to automation.
Responsibilities:
- Architect, design, and deploy agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, and related orchestration libraries.
- Build multi-agent systems capable of autonomous reasoning, planning, task delegation, and collaboration across cybersecurity functions.
- Implement agent-to-agent coordination strategies, including shared memory, messaging, goal decomposition, and tool-use patterns.
- Design and optimize Agent Development Kit (ADK)-based pipelines for secure, scalable agent deployment.
- Develop Retrieval-Augmented Generation (RAG) pipelines enabling agents to interact with real-time knowledge sources, logs, cybersecurity datasets, and enterprise APIs.
- Optimize vector embeddings, indexing strategies, and memory structures for high-accuracy decision support.
- Ensure grounded, auditable, and explainable outputs from LLM-based agents.
- Fine-tune, prompt-engineer, and configure LLMs/SLMs for specialized cybersecurity and automation tasks.
- Build reasoning, planning, and self-critique modules for agents to operate autonomously and safely.
- Integrate external LLM APIs, embeddings, synthetic data, and custom model endpoints.
Requirements:
- 5+ years total experience in software development, AI/ML engineering, or data science.
- 1+ year of Cybersecurity domain exposure, especially IAM (SailPoint, CyberArk) and SIEM/SOAR (Splunk, QRadar, etc.).
- 1+ year of hands-on experience building agentic AI or multi-agent applications, including LLM-driven workflows or reasoning systems.
- Strong Python skills and working knowledge of SQL.
- Direct experience with LLM/SLM APIs, embeddings, vector databases, RAG architecture, and memory systems.
- Experience deploying AI workloads on GCP (Vertex AI) and IBM WatsonX.
- Familiarity with agentic AI protocols, ADKs, LangGraph, AutoGen, or similar orchestration tools.
- Practical experience implementing Model Context Protocol (MCP) for agent-level context management.
- 1+ year experience with LangChain, LlamaIndex, OpenAI, Cohere, Anthropic, or similar frameworks.
Benefits:
- Health insurance
- Flexible work arrangements
- Professional development opportunities
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