AI/ML Engineer II
Tasks
- Build auto remediation workflows
- Build microservices and event-driven systems
- Build multi modal models for text logs and graphs
- Collaborate with Product Detection Engineering and Customer Success
- Conduct red team testing
- Create streaming feature pipelines
- Design and build AI layer for autonomous detection
- Design and deploy orchestration frameworks for agents
- Develop and productionize large language models
- Develop graph based reasoning engines
- Develop tool-calling agents
- Experiment with retrieval-augmented generation
- Implement RAG backed investigation workflows
- Manage latency budgets and fallback strategies
- Own evaluation and reliability for AI systems
- Perform prompt evaluation and fine tuning
- Translate attacker behavior into ML and rule based detections
Perks/Benefits
Skills/Tech-stack
API Design | AWS | Agent Orchestration | Agent systems | Azure | Detection and Response | Docker | EDR | Endpoint Detection and Response | Endpoint detection | Event Driven | Event management | Event-driven architecture | Fall Back Strategies | Fine Tuning | GCP | GRPC | GRPC API | Go | Graph Databases | Graph reasoning | Java | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Latency optimization | MDR | Microservices | Multi-Agent | Multi-Agent Systems | Prompt engineering | Python | RAG | REST | REST API | Red team | Red team testing | Retrieval-Augmented Generation | Rust | SIEM | SOAR | Security Information | Security Information and Event | Security Information and Event Management | Streaming Data
Education
Bachelor of Engineering | Bachelor of Science | Master of Engineering | Master of Science | PhD
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer | Software Engineer
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