DEUS EX MACHINA

Systems Engineer

• Design and build AI systems end to end across backend, data engineering, AI systems, and architecture.
• Build agentic AI systems using LLMs, retrieval, tools, memory, orchestration, and human-in-the-loop patterns.
• Decide when agents are appropriate and when deterministic software is better.
• Implement production AI systems across backend services, data infrastructure, agentic workflows, and cloud components.
• Design and build APIs, services, event-driven workflows, and distributed components connecting applications, models, and data.
• Build and operate pipelines for structured, unstructured, scientific, and external data, including ingestion, transformation, indexing, lineage, and quality controls.
• Design retrieval and knowledge architectures using relational databases, vector stores, search systems, knowledge graphs, and other data stores.
• Turn prototypes into production systems by defining interfaces, failure modes, observability, testing, security, performance, and deployment strategy.
• Make and document architectural decisions involving cost, reliability, maintainability, scalability, and model performance tradeoffs.
• Work directly with data scientists, bioinformaticians, product managers, and engineers while maintaining technical integrity of the complete system.
• Deliver major systems from feature ideation through production operation with few handoffs between backend, data, and AI teams.
• Bring new AI capabilities to production quickly without sacrificing reliability, testability, security, or maintainability.

AI Systems Engineer