Lead Software Engineer - Automated Detection/AI Proactive Defense

Description :

We value a culture of sharing, so every team has the opportunity to share their work with the entire department during our regular R& D Demos. Once a year, we hold a department-wide Hackathon, teaming up across all R& D teams over four days to collaborate and build cool ideas outside the normal project scope. While innovation is the focus, some of these ideas do make it into our products.

Responsibilities :

- Architect and implement agentic AI frameworks that enable autonomous reasoning, decision-making, and proactive threat response.

- Design scalable multi-agent coordination systems capable of planning, executing, and self-correcting across dynamic security workflows.

- Integrate LLMs, vector databases, and tool-use APIs to enable contextual memory, retrieval, and adaptive behavior in defence scenarios.

- Develop reusable orchestration layers for knowledge routing, task delegation, and inter-agent communication in SOC environments.

- Collaborate with cross-functional teams to productionize AI systems with observability, scalability, and resilience.

- Contribute to architectural strategy for agent state persistence, human-agent interaction, and external system integration.

- Prototype and evaluate emerging agentic frameworks (e. g., LangChain, Semantic Kernel, LlamaIndex, AutoGen, CrewAI) to evolve Arctic Wolf's proactive defence infrastructure.

- Provide technical leadership across the AI automation roadmap, ensuring design excellence and long-term maintainability.

Requirements :

- Experience in cybersecurity, SOC workflows, or threat intelligence.

- Familiarity with multi-agent systems and coordination strategies.

- Knowledge of LLM fine-tuning (LoRA, QLoRA) and RAG pipelines.

- Exposure to vector databases like FAISS or Pinecone.

- Understanding of AI safety, governance, and compliance.

- Contributions to open-source AI or automation projects.

- Strong technical communication and cross-functional leadership.

- 8+ years of experience in backend software development, distributed systems, or applied AI engineering.

- Proven expertise in building agentic AI frameworks, orchestration systems, or autonomous reasoning engines.

- Strong programming skills in Python, Go, or similar backend languages.

- Hands-on experience with LLMs and ecosystems like LangChain, Semantic Kernel, OpenAI/Anthropic APIs.

- Solid understanding of cloud-native architectures and microservices (AWS, GCP, or Azure).

- Passion for building generalizable agent infrastructure and self-improving AI systems.

- Experience mentoring developers and leading cross-functional initiatives through technical influence.

- Comfortable navigating ambiguity and balancing innovation with delivery in fast-paced AI environments.