Principal Software Development Engineer

About the Team & Role

• Own end-to-end systems architecture across data pipelines, AI/ML platforms, semantic layers, and application interfaces — designing for modularity, scale, and durability from day one.
• Build and evolve the data integration layer: ingestion, normalization, and orchestration across structured and unstructured sources, using API-first design principles (REST, GraphQL, gRPC) and real-time streaming technologies like Kafka and Apache Pulsar.
• Architect the semantic intelligence layer: knowledge graphs, ontology design, vector embeddings, and RAG techniques that give Auger context-aware reasoning across the full enterprise data fabric.
• Design and operate scalable AI/ML platforms for training, deployment, and model lifecycle management — integrating LLMs, embeddings, and multimodal models into production applications via MLOps tooling (MLflow, SageMaker, Databricks).
• Drive AI into the application layer: partner with product and design to ship agentic, adaptive user experiences that surface intelligence at the moment operators need it.
• Set architectural direction across the platform: make layer boundaries, evolution strategies, and tradeoffs explicit — and document decisions the team can execute against with confidence.
• Raise the bar on operational rigor: fault tolerance, high availability, observability, and performance at enterprise scale are non-negotiable properties, not afterthoughts.
• Mentor engineers on system design, coding standards, and operational excellence — and hold a high bar on what ships.

• Bachelor or Master's degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in systems architecture, software engineering, and platform development — with a proven track record building scalable data platforms or AI-driven systems at enterprise scale.
• Deep programming expertise in Python, Java, or C++, and hands-on experience building distributed systems in cloud-native environments (Azure, AWS, or GCP, including multi-cloud).
• Fluency in real-time data processing and analytics frameworks (Spark, Kafka, Flink) and big data technologies (Databricks, Snowflake, Hadoop).
• Advanced understanding of semantic modeling, knowledge graphs, and ontology design — including graph databases, graph embeddings, link prediction, and GNNs.
• Hands-on experience with AI/ML pipeline design and deployment, including frameworks such as TensorFlow, PyTorch, or equivalent, and familiarity with architectural patterns including microservices, event-driven architectures, and domain-driven design.
• Technical leadership through ambiguity: you set direction, communicate tradeoffs clearly to technical and non-technical partners, and write crisp architecture decisions when the stakes are high.