• Partner with stakeholders to translate complex customer challenges into robust technical designs and AI strategies • Architect, build, and deploy high-performance AI/ML platforms and tools, ensuring scalability, reliability, and security • Lead the development, evaluation, fine-tuning, and monitoring of production-grade AI/ML models (including LLMs and RAG architectures) • Design and standardize CI/CD pipelines (GitHub Actions) and Infrastructure as Code (Terraform) to ensure reproducible and automated deployments • Proactively monitor system health, lead incident response for critical AI systems, and drive architectural refactoring to remove bottlenecks • Mentor L1/L2 engineers, conduct code reviews, and foster a culture of engineering excellence and best practices • Evaluate emerging AI technologies/vendors and prototype new approaches to keep the organization at the cutting edge of the industry.

RegScale is a continuous controls monitoring (CCM) platform that helps organizations automate and scale their security, risk, and compliance programs. We are at an inflection point, transitioning from startup execution to a disciplined, enterprise ready engineering organization, and we are building the team that will take us there. AI is central to where RegScale is going, woven into how compliance programs are automated, monitored, and delivered at scale. The Role Senior AI Engineers at RegScale own the design and delivery of production AI systems end to end. You bring genuine breadth across the AI engineering stack including data pipelines, model fine tuning and evaluation, agent design and orchestration, MCP server development, and AI safeguards, and you understand how these disciplines connect. You make sound decisions across all of them, not just within a narrow specialization. Your work lives inside Platform Engineering and is consumed by product teams building GRC features and by integrators connecting RegScale to the broader compliance ecosystem. You build primitives and frameworks others build on top of, and you raise the bar for how the engineering organization thinks about and delivers AI in production. This role is for an engineer who brings the same rigor to AI systems as to any other production engineering discipline, including reliability, observability, cost management, and ongoing behavior in the real world. Key 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 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. Required Qualifications - 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. Preferred Qualifications - Experience building AI systems in regulated industries or compliance focused platforms where auditability, explainability, and data sensitivity shaped design decisions. - Familiarity with secure MCP server development and protocols for exposing platform capabilities to AI systems. - Background in enterprise SaaS companies where AI features had to meet enterprise reliability, security, and integration standards. - Experience with inference cost optimization, caching strategies, and model selection tradeoffs at scale. - Familiarity with GRC frameworks such as FedRAMP, NIST, or CMMC and the compliance domain more broadly. - Experience with cloud native AI infrastructure in Azure or comparable platforms including model deployment, scaling, and monitoring. - Contributions to or deep familiarity with open-source AI frameworks for orchestration, evaluation, and observability. - Experience implementing and using data lake solutions (i.e. Snowflake, Databricks, Synapse Analytics, AWS Redshift) in a production environment. RegScale is only able to hire US Citizens

• Build and ship LLM-powered features end-to-end: from prototype to production-ready systems (RAG, agents, tool calling, workflow automation) • Design retrieval and search pipelines using OpenSearch / Elasticsearch, including indexing strategies and query patterns that work for real user needs • Develop backend services and APIs in Python, using Pydantic for robust data validation and clear contracts • Orchestrate async and scheduled workloads (batch jobs, pipelines, background workers) with Celery / Prefect • Own data modeling and persistence for AI workflows using SQLAlchemy • Add observability and reliability with OpenTelemetry: tracing, metrics, and logs that make systems debuggable and safe to operate • Collaborate async-first with product and engineering: align on trade-offs, ship continuously, improve based on feedback and usage • Proactively identify edge cases and failure modes (hallucinations, retrieval misses, long-tail inputs, timeouts) and fix them with pragmatic engineering

• Build production-grade systems that operate in real-world clinical settings • Integrate intelligent systems directly into neonatal precision medicine