Senior AI/ML Engineer
Tasks
- Build RAG backed investigation workflows
- Build retrieval-augmented generation workflows
- Build streaming feature pipelines
- Conduct red team testing
- Contribute to architectural design reviews
- Create fallback strategies
- Design AI system architecture
- Develop AI layer for autonomous detection
- Develop and productionize large language models
- Develop auto-remediation workflows
- Develop graph based reasoning engines
- Develop multi modal models for text logs and graphs
- Implement evaluation and reliability for prompts and fine tuning
- Integrate tool calling agents
- Lead cross-functional engineering initiatives
- Mentor junior engineers
- Set latency budgets
- Translate attacker behavior into ML and rule based detections
Perks/Benefits
- N/A
Skills/Tech-stack
Agent systems | Agentic Systems | Data Pipelines | Docker | Driven systems | Evaluation Harness | Event Driven | Event Driven Systems | Fallback strategies | Fine Tuning | GRPC | Go | Graph Databases | Graph reasoning | Java | Kubernetes | LLM Tool Calling | Langchain | Langgraph | Language Models | Large Language Models | Latency optimization | Microservices | Multi-Agent | Multi-Agent Systems | Prompt engineering | Python | RAG | REST | Red team | Red team testing | Retrieval-Augmented Generation | Rust | Streaming Data | Streaming data pipelines | Tool-Calling
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