Senior AI Engineer - Cybersecurity Systems

About the Role

We are looking for a Senior AI Engineer to drive the design, architecture, development, and productionization of Generative AI, Agentic AI, and AI-driven automation solutions for enterprise cybersecurity products.

The ideal candidate should be able to independently own AI initiatives end-to-endfrom understanding the business problem and defining the AI approach to architecture, implementation, integration, evaluation, deployment, monitoring, and continuous improvement. The role requires strong hands-on engineering capability rather than research-only or API-integration experience.

Key Responsibilities

• Own AI/GenAI initiatives end-to-end, translating business and cybersecurity requirements into scalable AI solutions.
• Design and implement Agentic AI and Multi-Agent systems for autonomous investigation, decision support, workflow automation, threat analysis, and security operations.
• Architect and develop LLM-powered applications, RAG pipelines, AI agents, conversational systems, and intelligent automation workflows.
• Design agent orchestration including planning, tool/function calling, memory, state management, task decomposition, retries, human-in-the-loop, and guardrails.
• Build enterprise-grade RAG solutions, including document ingestion, preprocessing, chunking, embeddings, vector search, metadata filtering, reranking, and contextual response generation.
• Integrate AI agents with enterprise and cybersecurity systems through REST APIs, webhooks, SDKs, databases, SIEM/EDR/security tools, & third-party services.
• Establish AI evaluation mechanisms covering accuracy, relevance, groundedness, hallucination, tool-call accuracy, latency, reliability, & cost.
• Optimize LLM applications for performance, scalability, reliability, token consumption, latency, and inference cost.
• Design secure AI solutions addressing prompt injection, data leakage, access control, sensitive information handling, unsafe tool execution, and AI-specific security risks.
• Develop production-ready AI services and APIs using Python/FastAPI, with appropriate logging, error handling, testing, observability, and monitoring.
• Independently troubleshoot complex issues across LLM, RAG, agent orchestration, integrations, APIs, databases, and deployment environments.
• Mentor AI engineers and contribute to AI architecture standards, reusable frameworks, engineering best practices, and technical decision-making.

Must-Have Skills & Experience

AI / GenAI / Agentic AI

• 5+ years total software/engineering experience, with at least 2+ years hands-on experience in GenAI and Agentic AI.
• Strong practical experience building LLM-based production applications and AI agents.
• Strong understanding of Agentic AI, Multi-Agent Systems, AI workflows, tool calling/function calling, planning, memory, state management, human-in-the-loop patterns and fine-tuning.
• Hands-on experience with LangChain and/or LangGraph; experience with CrewAI, AutoGen, Agno, LlamaIndex or similar frameworks is a plus.
• Strong expertise in RAG architecture, embeddings, vector search, hybrid search, reranking, and knowledge-base design.
• Hands-on experience with vector databases such as Qdrant, Milvus, Pinecone, Weaviate, Chroma or OpenSearch.
• Strong prompt engineering skills, including structured outputs, few-shot prompting, prompt optimization, and context engineering.
• Experience with LLM evaluation, hallucination reduction, guardrails, AI observability, and production monitoring.
• Experience with LLM providers/models such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, Mistral or equivalent.
• Strong Python programming skills with FastAPI, Pydantic, async programming, REST APIs, and API integration.
• Working knowledge of SQL/NoSQL databases, preferably MySQL/PostgreSQL
• Experience with Docker and cloud-based AI deployment, preferably AWS.
• Strong understanding of Git, CI/CD, testing, debugging, logging, and production support.

Cybersecurity Preferred

• Experience building AI solutions for SOC, SIEM, EDR/XDR, vulnerability management, threat intelligence, incident response, or attack surface management.
• Understanding of CVE/CVSS, MITRE ATT&CK, threat intelligence, security alerts, vulnerabilities, and security operations workflows is highly desirable.

Good to Have

• Basic understanding of JavaScript/TypeScript, React, and modern web application architecture.
• Working knowledge of AWS services such as ECS/EKS, Lambda, S3, Bedrock, RDS, OpenSearch, and CloudWatch.
• Experience with MCP (Model Context Protocol) and modern agent/tool integration patterns.
• Exposure to LoRA/PEFT, open-source LLMs, model serving, or inference optimization.