Summary
What you’ll impact
The Principal Engineer for the platform will lead the design and implementation of the organization’s data lake and AI platform, overseeing architecture across data pipelines, AI/ML systems, and semantic layers. This role involves building scalable, modular, and highly available infrastructure while mentoring engineers and driving AI‑enabled user experiences.
Responsibilities
What you'll do
- 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.
Requirements
What you’ll bring
- 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.