We are looking for a Senior AI Engineer to build AI capabilities for our products, helping security teams explore data, analyze alerts, and investigate incidents more effectively.
You will design, implement, and deliver AI features while providing technical direction within your area of ownership. This role suits someone with strong
software engineering fundamentals, practical experience building LLM applications, and an interest in solving real cybersecurity problems.
Key responsibilities
Design and build LLM-powered systems, including retrieval workflows, context management, tool calling, and structured outputs.
Develop AI agents that support natural-language log queries, evidence collection, and investigation summaries grounded in source data.
Define appropriate automation boundaries and introduce human approval for sensitive actions.
Integrate AI capabilities with product APIs, backend services, and data sources.
Partner with
Data Scientists to build evaluation datasets from real scenarios, analyze failures, and establish regression checks for model, prompt, and workflow changes.
Improve response quality, latency, and operating costs; implement monitoring and diagnostics for production systems.
Implement access controls for agent tools and data, with reliable handling of missing evidence, invalid queries, and tool failures.
Review technical designs and code, mentor AI Engineers, and develop reusable components for the team.
Collaborate with Product, Backend, and security specialists to translate operational needs into features with clear acceptance criteria.
Qualifications
Education: Bachelor's degree/ Master's degree or in Computer Science, Artificial Intelligence, Machine Learning,
Electrical Engineering, or a related field.
6+ years of experience
, including at least 3
years building and operating LLM applications in production
.
Strong Python skills and experience designing APIs, working with databases, and deploying services.
Hands-on experience with RAG, vector search, prompt engineering, and agent workflows.
Ability to design solutions from requirements through deployment and explain trade-offs involving reliability, performance, and cost.
A systematic approach to evaluating AI quality using data and experiments.
Demonstrated ownership across the feature lifecycle: requirements, implementation, testing, release, and continuous improvement.
Clear communication, proactive problem-solving, and the ability to mentor teammates.
Ability to read technical documentation and communicate effectively in English.
Nice to Have:
Familiarity with SOC operations, alert triage, incident investigation, or threat hunting.
Experience processing security logs, integrating SIEM/SOAR platforms, or building tools for security analysts.
Experience addressing prompt injection, data leakage, or risks associated with agent tool use.
Experience building long-running workflows with state persistence, failure recovery, and execution tracing.
Open-source contributions or projects that demonstrate practical AI engineering skills.