Principal AI Tech Lead

AI Technical Lead

Required

Skills & Experience:

10+ years of software engineering experience; 5+ years in technical leadership or architect roles. Production experience leading and shipping agentic AI products end-to-end — architecture, build, ship, operate. Deep expertise across the AI Platform stack: agents (Agent Core, Strands, Bedrock), RAG, knowledge graphs, lakehouse, governance. Strong harness engineering experience — has built reusable evaluation, testing, and observability infrastructure for AI systems.

Demonstrated ability to operate at both the architectural level and the code level on complex AI systems. Strong AWS experience across the AI-native stack:
Bedrock, Agent Core, Neptune, Athena, Redshift, Lake Formation, DMS, Aurora. Experience leading the technical direction of an AI team through influence — architecture, code, and design rigor — without relying on direct people-management authority. Strong, articulate understanding of the differences between leading software teams and leading AI teams: eval-driven over test-driven, probabilistic systems, model selection economics, faster iteration cycles, and the tight coupling between data quality and model quality.

Production experience in multi-tenant SaaS ellent written communication — able to author architectural RFCs, decision records, and technical strategy documents that align cross-functional stakeholders. Strong stakeholder skills — comfortable in deep technical discussions and in conversations with senior leadership, including PE/board audiences.

Nice to Have

Skills & Experience:

Legal tech background or experience in another regulated industry such as healthcare or financial services. Familiarity with ABA Model Rules of Professional Conduct and ABA Formal Opinion 512, or equivalent AI-in-legal regulatory frameworks. Experience working with PE-backed companies through scaling phases. Experience evaluating and operationalizing AWS Agent Core and Anthropic Claude Managed Agents in production. Direct experience building or operating multi-tenant agent platforms with microVM-level tenant isolation.

Experience implementing the trust architecture pillars: ethical AI, tenant isolation, observability and audit, explainability with citations, hallucination guardrails. Public technical voice — speaker, writer, or open-source contributor on AI engineering topics.

Job Description:

This AI Technical Lead will own the end-to-end architecture of an AI platform, including agent frameworks, retrieval systems, knowledge graphs, lakehouse architecture, governance models, and the overall learning flywheel that drives continuous improvement. The role is highly strategic and highly technical, with approximately 50% of time spent hands-on coding and building foundational frameworks, prototypes, evaluation harnesses, and core platform capabilities, while the remaining 50% focuses on architecture leadership, technical roadmaps, design reviews, RFCs, vendor evaluations, and cross-team alignment.

This individual will be the senior technical authority for complex architectural discussions, leading tradeoff decisions and elevating engineering standards across the AI organization. They will own the agentic architecture, including Bedrock, Agent Core, Strands, multi-agent orchestration, tool integration, MCP design, and AI guardrails. The role also carries responsibility for platform evaluation practices, retrieval quality, agent performance measurement, data quality standards, and trust architecture requirements such as tenant isolation, auditability, explainability, compliance, and hallucination prevention within a regulated legal services environment.

Additionally, this leader will mentor senior engineers across data, graph, retrieval, and agent disciplines while partnering closely with product, security, legal, and executive leadership to shape long-term AI strategy, staffing plans, and technology investments, making recommendations across vendors and platforms such as Anthropic, AWS, OpenAI, and other emerging technologies based on scalability, cost, and regulatory considerations.