Senior AI Engineer

About the Role\n\nWe are looking for a Senior AI Engineer to design and develop advanced AI-driven cybersecurity systems capable of reasoning over vulnerability detection, complex security data analysis, analyst assistance, and automation of security operations workflows.\nThis role extends beyond traditional AI feature development and focuses on building intelligent systems that can understand security context, analyze threats, support investigations, and execute multi-step decision workflows with appropriate safeguards.\nThe ideal candidate should possess strong AI engineering expertise along with practical cybersecurity knowledge and be capable of working across the complete AI lifecycle—from model architecture and training to deployment of production-grade intelligent systems.\n\nKey Responsibilities\n\nDesign and develop AI-powered cybersecurity capabilities for threat analysis, incident investigation, vulnerability intelligence, and security operations automation.\nBuild intelligent systems capable of reasoning across structured and unstructured security data sources, including telemetry, alerts, threat intelligence, security knowledge bases, and operational workflows.\nDesign, train, and optimize custom LLMs or domain-specific language models for cybersecurity applications, including model architecture decisions, training pipeline development, and evaluation.\nDevelop and optimize LLM-based applications, including pretraining, fine-tuning, evaluation frameworks, and inference optimization.\nContribute to the design of AI architectures involving retrieval, reasoning, tool usage, planning, and controlled execution of security workflows.\nBuild systems capable of orchestrating multi-step tasks such as investigation support, enrichment, correlation, summarization, and guided response actions.\nCollaborate closely with cybersecurity teams to translate operational workflows into reliable AI-assisted or autonomous workflows.\nDesign safeguards, access controls, and validation mechanisms for AI-driven security actions.\nEvaluate system performance for accuracy, reliability, reasoning quality, latency, and operational effectiveness.\nStay updated with advancements in LLMs, agentic AI systems, autonomous workflows, and AI security.\n\nRequired Qualifications\n\nBachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Cybersecurity, or a related discipline.\n6+ years of experience in software engineering or machine learning engineering.\n3+ years of hands-on experience building production AI/ML systems.\nStrong hands-on experience with PyTorch and modern LLM development ecosystems.\nDemonstrated experience building or training language models beyond API consumption or basic fine-tuning, including understanding of tokenizer design, transformer architectures, pretraining workflows, and model evaluation.\nCapability to design and develop custom LLMs from scratch for domain-specific use cases, where required.\nStrong understanding of distributed training concepts, model optimization, and inference serving.\nExperience designing AI systems that interact with tools, APIs, external knowledge sources, or operational systems.\nPractical understanding of cybersecurity domains such as SOC operations, threat intelligence, incident response, vulnerability management, or detection engineering.\nExperience building scalable backend systems for production workloads.\n\nPreferred Experience\n\nStrong preference will be given to candidates with experience in one or more of the following areas:\nAgentic AI systems and autonomous workflow orchestration\nMulti-agent architectures for reasoning or task execution\nRetrieval-Augmented Generation (RAG) and knowledge-grounded AI systems\nTool-calling / function-execution architectures\nHuman-in-the-loop decision systems\nGraph-based reasoning or knowledge graph integration\nPrompt engineering and structured orchestration patterns\nGPU infrastructure and inference optimization\nKubernetes, Docker, and cloud-native deployment