Senior AI Engineer

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🕒 July 27

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RegScale

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11 - 50 employees

💰 $20M Series A on 2022-08

RegScale overcomes speed, timeliness, and cost effectiveness limitations in legacy GRC by bridging security, risk, and compliance through our Continuous Controls Monitoring platform.

📋 Description

• 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 ingest, clean, transform, and version the data AI systems depend on, ensuring quality and traceability from source to model. • Design and implement retrieval augmented generation pipelines, vector and graph search systems, and hybrid retrieval strategies that make compliance data accessible for AI driven features. • Fine tune, evaluate, and monitor models against real world performance criteria, with a clear understanding of how to measure what matters in a compliance domain. • Architect and build AI agent systems and orchestration layers that coordinate multi step reasoning, tool use, and decision making across complex GRC workflows. • Build and maintain MCP servers that expose RegScale platform capabilities to AI systems, enabling reliable, secure, and observable AI integrations across the platform. • Design reusable AI primitives and frameworks that product and integration teams can build on, accelerating AI feature development across the organization. • Integrate AI capabilities into CI/CD pipelines with appropriate testing, evaluation gates, and deployment strategies that maintain production quality as models and data evolve. • Partner with Platform Engineering, Core Engineering, and Compliance as Code teams to ensure AI capabilities meet enterprise reliability and security standards. • Proactively identify risks in AI system behavior, data quality, and model performance, bringing proposed mitigations before they become production incidents.

🎯 Requirements

• 8 or more years of software engineering experience with at least 4 years focused on building and operating AI or machine learning systems in production environments. • Demonstrated track record of shipping AI features that customers depend on, with ownership across the full production lifecycle including reliability, observability, cost management, and ongoing model behavior. • Strong data engineering fundamentals, including pipeline design, data modeling, transformation, quality validation, and performance monitoring at scale. • Hands on experience with retrieval augmented generation, vector and graph databases, embedding models, and hybrid retrieval strategies. • Experience designing and building AI agent systems and orchestration frameworks, including multi step reasoning, tool use, and failure handling in production contexts. • Solid understanding of model fine tuning and evaluation, including how to define meaningful performance criteria for domain specific applications. • Strong software engineering fundamentals applied to AI systems with production grade rigor. • Strong written and verbal communication skills, able to articulate AI architecture decisions and tradeoffs to both technical and non-technical stakeholders.