Senior Principal Engineer, Agentic Systems

About the role

Own the technical architecture for your team's product surface — service boundaries, data flow, API design, infrastructure decisions, and scaling strategy for large-scale SaaS.
Review and approve product specifications before build begins — your job is to ensure specs give both humans and AI agents enough technical context to execute well.
Author and maintain the team's agentic ecosystem — CLAUDE.md files, MCP server configurations, custom slash commands, agent hooks, and workflow templates. These artifacts teach AI agents how to work in your codebase and are the single most leveraged system you own.
Build and own the AI-powered test automation pipeline — consume the Product Lead's user stories and acceptance criteria to drive automated e2e test generation, regression suites, and CI-integrated quality gates.
Review agent-generated code for architecture alignment, security, performance, and correctness — agents produce volume, you provide judgment.
Run the compound loop — after each feature ships, encode what worked and what didn't back into the system so agents and engineers get better with every cycle.
Make architecture decisions that hold up at scale — real-time data processing, cloud-native infrastructure, API design for high-throughput environments.
Write code selectively — architecturally novel problems, security-critical paths, and reference implementations that set the patterns agents follow.
Direct AI agents on well-scoped implementation tasks while personally handling the work that requires deep system understanding.

Responsibilities

• Own the technical architecture for your team's product surface — service boundaries, data flow, API design, infrastructure decisions, and scaling strategy for large-scale SaaS
• Review and approve product specifications before build begins — your job is to ensure specs give both humans and AI agents enough technical context to execute well
• Author and maintain the team's agentic ecosystem — CLAUDE.md files, MCP server configurations, custom slash commands, agent hooks, and workflow templates
• Build and own the AI-powered test automation pipeline — consume the Product Lead's user stories and acceptance criteria to drive automated e2e test generation, regression suites, and CI-integrated quality gates
• Review agent-generated code for architecture alignment, security, performance, and correctness — agents produce volume, you provide judgment
• Run the compound loop — after each feature ships, encode what worked and what didn't back into the system so agents and engineers get better with every cycle
• Make architecture decisions that hold up at scale — real-time data processing, cloud-native infrastructure, API design for high-throughput environments
• Write code selectively — architecturally novel problems, security-critical paths, and reference implementations that set the patterns agents follow
• Direct AI agents on well-scoped implementation tasks while personally handling the work that requires deep system understanding

Requirements

8+ years as a software engineer, with at least 2 years in a tech lead, staff engineer, or architect role at a large-scale B2B SaaS company
Deep architecture experience — you've designed and shipped systems involving real-time data processing, cloud infrastructure, distributed services, and API-driven platforms
Strong cloud infrastructure depth — AWS (or equivalent), streaming architectures, large-scale data processing. You've built systems that handle significant throughput in production
Hands-on engineering ability — you can still build, not just draw diagrams. You write code when it matters
Experience with AI coding tools — you've used Claude Code, Cursor, or similar and have opinions on where agents help vs. where they fail
Strong system design instincts — can articulate *why* an architecture decision is right, not just *what* it is
Experience with AI technologies, LLMs, ASR, OCR, RAG
Experience designing automated pipelines that connect multiple systems (CI/CD, monitoring, alerting, data platforms) into cohesive engineering workflows
Experience building test automation infrastructure — frameworks, e2e pipelines, CI-integrated quality gates

Qualifications

Preferred:
Hands-on experience with MCP (Model Context Protocol) or similar tool-integration patterns for LLMs
Experience with workflow orchestration platforms (Temporal, custom pipelines)

Skills

Experience in financial services, wealth management, or regulated industries

Benefits

Comprehensive health benefits, including dental, life, and disability insurance
Unlimited paid time off program

Pay

The estimated base salary range for this position is $200,000 – $300,000 + bonus & equity.

Schedule

Remote work is available in the following states: CA, CT, FL, GA, IL, MA, MD, ME, MI, NH, NJ, NY, NC, PA, RI, SC, TN, TX, UT, WA, VA.