Principal AI Engineer - Nexus Black at IFS1

We are seeking a Principal AI Engineer with a builder’s mindset to define and lead the technical direction of AI agents and intelligent workflows for IFS Nexus Black. You will operate at the intersection of product strategy, system architecture, and hands-on execution—turning ambiguous customer and business problems into scalable AI capabilities that move the company forward. This role requires deep production experience with LLM/agent systems, strong architectural judgment, and the ability to influence across teams. You will set the bar for how we design, evaluate, deploy, and scale AI systems safely and reliably. Key Responsibilities 1. Technical Strategy & Architecture Leadership • Define the long-term architecture for AI agents, orchestration systems, retrieval pipelines, evaluation frameworks, and safety guardrails. • Establish technical standards and best practices for LLM usage, agent planning/execution, tool integration, observability, and governance. • Make high-leverage architectural decisions that balance innovation, reliability, cost, and security. • Drive build-vs-buy strategy across AI infrastructure (vector stores, orchestration layers, eval tooling, hosting, etc.). 2. 0→1 Innovation to Scaled Systems • Lead rapid prototyping of AI agents and workflows; validate value with customers. • Architect and guide the transition from experimental POCs to resilient, scalable production systems. • Identify reusable primitives (agent frameworks, SDKs, evaluation harnesses) that accelerate future development. • Ensure systems are designed for maintainability, extensibility, and enterprise-grade reliability. 3. Cross-Functional & Cross-Org Influence • Partner with product leadership to shape AI strategy and roadmap. • Translate ambiguous business goals into technical investment areas and measurable outcomes. • Collaborate with data engineering, platform, and security teams to align infrastructure and governance. • Represent AI technical direction in executive discussions and customer engagements. 4. Evaluation, Safety & Quality at Scale • Design and institutionalize evaluation loops (offline benchmarks, regression suites, online experimentation, human review systems). • Establish quality metrics, golden sets, and safety guardrails across agent systems. • Lead efforts in observability, tracing, and failure analysis for AI workflows. • Ensure responsible AI practices and compliance requirements are embedded into system design. 5. Technical Leadership & Talent Development • Lead architectural reviews and raise the engineering quality bar. • Mentor senior engineers and shape engineering culture. • Drive alignment across teams building AI-enabled features. • Contribute to hiring and calibration for high-impact AI roles.