2 months ago
Remote, WorldwideSenior / Staff+
Responsibilities
- Design, build, and operate AI systems in production with full ownership across reliability, performance, cost, observability, and ongoing model behavior.
- Build and maintain data pipelines that ensure quality and traceability from source to model.
- Design and implement retrieval augmented generation pipelines and hybrid retrieval strategies for compliance data.
- Fine tune, evaluate, and monitor models against real world performance criteria.
- Architect and build AI agent systems that coordinate multi-step reasoning and decision making.
- Build and maintain MCP servers for secure AI integrations across the platform.
- Design reusable AI primitives and frameworks for product and integration teams.
- Integrate AI capabilities into CI/CD pipelines with testing and evaluation gates.
- Partner with engineering teams to ensure AI capabilities meet enterprise standards.
- Proactively identify risks in AI system behavior and data quality.
Requirements
- 8 or more years of software engineering experience with at least 4 years focused on AI or machine learning systems.
- Demonstrated track record of shipping AI features with ownership across the full production lifecycle.
- Strong data engineering fundamentals including pipeline design and performance monitoring.
- Hands-on experience with retrieval augmented generation and hybrid retrieval strategies.
- Experience designing AI agent systems and orchestration frameworks.
- Solid understanding of model fine tuning and evaluation for domain-specific applications.
- Strong software engineering fundamentals applied to AI systems.
- Strong written and verbal communication skills for articulating AI architecture decisions.
Tech Stack
Amazon RedshiftAzureDatabricksSnowflake
Categories
AI & MLData Engineering