Senior AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and manage AI systems in production, taking full responsibility for reliability, performance, cost, observability, and continuous model behavior.

• Construct and uphold data pipelines that ingest, clean, transform, and version the data essential for AI systems, ensuring quality and traceability from source to model.

• Create and implement retrieval augmented generation pipelines, vector and graph search systems, and hybrid retrieval strategies to make compliance data accessible for AI-driven features.

• Fine-tune, assess, and monitor models against real-world performance criteria, with a clear grasp of how to measure critical factors in a compliance domain.

• Design and build AI agent systems and orchestration layers that facilitate multi-step reasoning, tool usage, and decision-making across intricate GRC workflows.

• Develop and maintain MCP servers that expose RegScale platform capabilities to AI systems, ensuring reliable, secure, and observable AI integrations across the platform.

• Create reusable AI primitives and frameworks that product and integration teams can leverage, expediting AI feature development across the organization.

• Incorporate AI capabilities into CI/CD pipelines with suitable testing, evaluation gates, and deployment strategies that uphold production quality as models and data evolve.

• Collaborate with Platform Engineering, Core Engineering, and Compliance as Code teams to ensure AI capabilities align with enterprise reliability and security standards.

• Actively identify risks in AI system behavior, data quality, and model performance, proposing mitigations before they escalate into production incidents.

⛳️ Requirements

• A minimum of 8 years of software engineering experience, with at least 4 years dedicated to building and operating AI or machine learning systems in production environments.

• Proven history of delivering AI features that customers rely on, with ownership across the entire production lifecycle, including reliability, observability, cost management, and ongoing model behavior.

• Strong data engineering principles, encompassing pipeline design, data modeling, transformation, quality validation, and performance monitoring at scale.

• Practical experience with retrieval augmented generation, vector and graph databases, embedding models, and hybrid retrieval strategies.

• Experience in designing and developing AI agent systems and orchestration frameworks, including multi-step reasoning, tool usage, and failure management in production contexts.

• Thorough understanding of model fine-tuning and evaluation, including the ability to define meaningful performance criteria for domain-specific applications.

• Strong software engineering principles applied to AI systems with a focus on production-grade rigor.

• Excellent written and verbal communication skills, capable of conveying AI architecture decisions and trade-offs to both technical and non-technical stakeholders.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health insurance and wellness programs.

• Opportunities for professional development and continuous learning.

• Flexible work schedules and remote work options.

• Collaborative and innovative work environment.