AI Engineer

We are building Cognium an enterprise-grade platform for building deploying orchestrating and governing AI agents at scale. As Principal Architect you will own the end-to-end system design and make every foundational technical decision: from the distributed architecture (Kubernetes Temporal Kafka NATS) to the AI-native patterns (RAG pipelines LLM routing multi-agent orchestration guardrail frameworks). This is not a slide-deck architect role you will write code review every critical PR and personally build the hardest will be the technical conscience of the platform. When the team debates Temporal vs. a custom state machine Kafka vs. Pulsar pgvector vs. Weaviate or monolith vs. microservices your judgment settles it backed by hands-on prototyping not just theory System Architecture & Design Own the end-to-end architecture of the Cognium platform across all layers: API Gateway (Envoy) Agent Orchestration (Temporal) Agent Runtime LLM Router RAG Engine Tool Gateway Policy Engine Observability and Infrastructure. Design and maintain the logical architecture layers: Presentation API Gateway Security Pipeline Orchestration Runtime LLM/RAG/Tools Persistence Infrastructure. Define the Control Plane vs. Data Plane separation: global metadata (CockroachDB) vs. per-region execution (PostgreSQLCitus Redis pgvector). Hands-On Technical Leadership Personally design and implement the most complex subsystems: LLM Router (smart routing fallback chains A/B testing) multi-agent orchestration engine (supervisor pattern handoff protocol shared scratchpad) and the security pipeline (prompt injection defense guardrail framework). Write production code in Go (performance-critical services) Java/Spring Boot (business logic services) and Python (ML pipelines RAG engine). Expected contribution: 40-50% hands-on coding in the first 12 months AI/ML Architecture Design the RAG pipeline architecture: document processing chunking embedding hybrid retrieval (pgvector Elasticsearch BM25) RRF fusion re-ranking citation building. Own the RAGAS quality framework (Faithfulness Relevancy Precision Recall). Architect the LLM Router for model-agnostic operation: unified invocation interface across Anthropic OpenAI Google Mistral and self-hosted models (vLLM/TGI). Design routing rules fallback chains and A/B testing infrastructure