Principal Engineer (Data & Architecture)

Design the AWS data architecture end-to-end: select the right storage and database approach per use case (object storage, transactional, analytical, vector), and structure data for downstream AI consumption.
Integrate Salesforce with the AWS data layer: evaluate technical decisions around Salesforce migrations and bidirectional data flows, leveraging existing native services where appropriate.
Architect voice infrastructure on AWS: design transcription storage and integrate call flow data into the broader data pipeline.
Define how data is served to AI models: structure, index, and serve data for LLM consumption; design retrieval and grounding strategies appropriate to the problem.
Design the agent orchestration approach: how agents access data, sequence work, and route exceptions back to human operators.
Evaluate the AWS migration consulting proposal: identify overengineered components, project post-migration costs, propose alternatives, and represent the client's technical interests against vendor incentives.
Gather requirements directly from CEO, CTO, and AI/ML lead and translate business processes and data flows into architecture documentation that becomes the foundation for future development.

Principal-level professional with 5+ years of end-to-end ownership of enterprise data architectures on AWS
C1 English level or higher (written and spoken)
Deep hands-on expertise across AWS data services, with judgment to select the right tool per use case (object storage, transactional DBs, analytical engines, vector stores).
Production experience with AWS voice / contact center infrastructure and integrating it into downstream data pipelines.
Experience evaluating technical decisions on Salesforce migrations (custom objects, APIs, integration patterns, headless approaches).
Proven experience designing AI grounding and retrieval systems for LLMs in production (RAG, embeddings, retrieval pipelines).
Experience with multi-model orchestration: sequencing model outputs, preventing data conflicts, coordinating agents.
AWS cost architecture: reading bills, projecting costs, identifying cheaper alternatives.
Demonstrated track record of pushing back on vendor or consulting proposals for cloud migrations