As a Senior AI Engineer, you will be the primary architect of Cognna’s autonomous agent reasoning engine and the high-scale inference infrastructure that powers it. You are responsible for building production-grade reasoning systems that proactively plan, use tools, and collaborate. You will own the full lifecycle of our specialized security models, from domain-specific fine-tuning to architecting distributed, high-throughput inference services that serve as Security-core intelligence in our platform.
**Key Responsibilities
- Agentic Architecture & Multi-Agent Coordination**
- Autonomous Orchestration: Design stateful, multi-agent systems using frameworks like Google ADK.
- Protocol-First Integration: Architect and scale MCP servers and A2A interfaces, ensuring a decoupled and extensible agent ecosystem.
- Cognitive Optimization: Develop lean, high-reasoning microservices for deep reasoning, optimizing context token usage to maintain high planning accuracy with minimal latency.
2. Model Adaptation & Performance Engineering
- Specialized Fine-Tuning: Lead the architectural strategy for fine-tuning open-source and proprietary models on massive cybersecurity-specific telemetry.
- Advanced Training Regimes: Implement Quantization-Aware Training (QAT) and manage Adapter-based architectures to enable the dynamic loading of task-specific specialists without the overhead of full-model swaps.
- Evaluation Frameworks: Engineer rigorous, automated evaluation harnesses (including Human annotations and AI-judge patterns) to measure agent groundedness and resilience against the Security Engineer’s adversarial attack trees.
3. Production Inference & MLOps at Scale
- Distributed Inference Systems: Architect and maintain high-concurrency inference services using engines like vLLM, TGI, or TensorRT-LLM.
- Infrastructure Orchestration: Own the GPU/TPU resource management strategy.
- Observability & Debugging: Implement deep-trace observability for non-deterministic agentic workflows, providing the visibility needed to debug complex multi-step reasoning failures in production.
4. Advanced RAG & Semantic Intelligence
- Hybrid Retrieval Architectures: Design and optimize RAG pipelines involving graph-like data structures, agent-based knowledge retrieval and semantic searches.
- Memory Management: Architect episodic and persistent memory systems for agents, allowing for long-running security investigations that persist context across sessions.
Requirements
- Experience & Education: 5-7+ years in AI/ML Engineering or Backend Systems. Must have contributed to large-scale AI/ML inference service in production. M.S. or B.S. in Computer Science/AI is preferred.
- Inference Orchestration: KV-cache management, quantization formats like AWQ/FP8, and distributed serving across multi-node GPU clusters).
- Agentic Development: Expert in building autonomous systems using Google ADK/Langgraph/Langchain and experienced with AI Observervability frameworks like LangSmith or Langfuse. Hands-on experience building AI applications with MCP and A2A protocols.
- Cloud AI Native: Proficiency in Google Cloud (Vertex AI), including custom training pipelines, high-performance prediction endpoints, and the broader MLOps suite.
- Programming: Python and experience with high-performance backends (Go/C++) for inference optimization. You are comfortable working in a Kubernetes-native environment.
- CI/CD: You are comfortable working in a Kubernetes-native environment.
**Benefits
Competitive Package** – Salary + equity options + performance incentives
Flexible & Remote – Work from anywhere with an outcomes-first culture
Team of Experts – Work with designers, engineers, and security pros solving real-world problems
Growth-Focused – Your ideas ship, your voice counts, your growth matters
Global Impact – Build products that protect critical systems and data