Senior AI Engineer

Lumen Dynamics 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 Lumen Dynamics is going, woven into how compliance programs are automated, monitored, and delivered at scale. The Role Senior AI Engineers at Lumen Dynamics 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 Lumen Dynamics 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 Lumen Dynamics 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 domai