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Senior AI Security Engineer, Cyber Architecture, OT and Engineering

Security EngineerSecurity EngineerFull TimeRemoteSeniorTeam 10,001+Since 1989H1B SponsorCompany SiteLinkedIn

Location

India

Posted

19 days ago

Salary

0

Seniority

Senior

Job Description

Senior AI Security Engineer, Cyber Architecture, OT and Engineering

EY

• 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. • Lead the development of an enterprise-grade platform enabling orchestration of LLMs, RAG components, vector databases, and multi-agent protocols. • Standardize the use of Model Context Protocol (MCP) for consistent context-sharing, memory management, and interoperability across agents. • Build reusable agent templates, toolkits, and internal libraries to accelerate development across Cyber teams. • Implement CI/CD, pipeline orchestration, versioning, and agent lifecycle management. • Establish monitoring, tracing, and observability practices for autonomous system behavior. • Automate manual cybersecurity processes through AI-driven workflow orchestration and dynamic agents. • Extract, transform, and aggregate data from disparate cybersecurity sources such as SIEM, IAM, SOAR, endpoint telemetry, and API-driven security tools. • Apply ML and statistical modeling techniques for anomaly detection, classification, optimization, and pattern recognition. • Translate complex findings into intuitive visualizations and actionable insights for leadership. • Work with cybersecurity SMEs, analysts, and engineers to identify opportunities for autonomous decision systems. • Communicate complex AI concepts clearly to technical and non-technical audiences. • Drive innovation and advocate for emerging AI technologies across the organization.

Job 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
  • Preferred 2+ years developing automation or RPA solutions
  • 2+ years building on AWS, including serverless architectures
  • Demonstrated experience with data visualization platforms (Tableau, Power BI, Looker)
  • 2+ years working with APIs, microservices, and modern data engineering tooling
  • Applied experience with agile software development practices
  • Prior work deploying enterprise-scale agentic AI or autonomous reasoning systems
  • Contributions to open-source AI/ML or agentic frameworks.

Benefits

  • Competitive salary
  • Health insurance
  • 401(k) matching
  • Flexible work hours
  • Paid time off
  • Remote work options

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