Senior AI Engineer

About the Role

We 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.

This 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.

The 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.

Key Responsibilities

• Design and develop AI-powered cybersecurity capabilities for threat analysis, incident investigation, vulnerability intelligence, and security operations automation.
• Build intelligent systems capable of reasoning across structured and unstructured security data sources, including telemetry, alerts, threat intelligence, security knowledge bases, and operational workflows.
• Design, train, and optimize custom LLMs or domain-specific language models for cybersecurity applications, including model architecture decisions, training pipeline development, and evaluation.
• Develop and optimize LLM-based applications, including pretraining, fine-tuning, evaluation frameworks, and inference optimization.
• Contribute to the design of AI architectures involving retrieval, reasoning, tool usage, planning, and controlled execution of security workflows.
• Build systems capable of orchestrating multi-step tasks such as investigation support, enrichment, correlation, summarization, and guided response actions.
• Collaborate closely with cybersecurity teams to translate operational workflows into reliable AI-assisted or autonomous workflows.
• Design safeguards, access controls, and validation mechanisms for AI-driven security actions.
• Evaluate system performance for accuracy, reliability, reasoning quality, latency, and operational effectiveness.
• Stay updated with advancements in LLMs, agentic AI systems, autonomous workflows, and AI security.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Cybersecurity, or a related discipline.
• 6+ years of experience in software engineering or machine learning engineering.
• 3+ years of hands-on experience building production AI/ML systems.
• Strong hands-on experience with PyTorch and modern LLM development ecosystems.
• Demonstrated 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.
• Capability to design and develop custom LLMs from scratch for domain-specific use cases, where required.
• Strong understanding of distributed training concepts, model optimization, and inference serving.
• Experience designing AI systems that interact with tools, APIs, external knowledge sources, or operational systems.
• Practical understanding of cybersecurity domains such as SOC operations, threat intelligence, incident response, vulnerability management, or detection engineering.
• Experience building scalable backend systems for production workloads.

Preferred Experience

Strong preference will be given to candidates with experience in one or more of the following areas:

• Agentic AI systems and autonomous workflow orchestration
• Multi-agent architectures for reasoning or task execution
• Retrieval-Augmented Generation (RAG) and knowledge-grounded AI systems
• Tool-calling / function-execution architectures
• Human-in-the-loop decision systems
• Graph-based reasoning or knowledge graph integration
• Prompt engineering and structured orchestration patterns
• GPU infrastructure and inference optimization
• Kubernetes, Docker, and cloud-native deployment