AI-First Core IT Software Engineering: Software, ML & Data (Staff – Principal)

About the Role

We are hiring AI Engineers at multiple levels (Lead/Staff through Senior Principal) to join our IT Business Applications team. This group builds and operates the technology platforms that power Marketing, Sales, and the full Lead-to-Cash lifecycle. This is a solutions engineering role, not a data science or ML research role. You will act as a hands-on technical leader who uses AI as building blocks—LLMs, agents, RAG, embeddings—to deliver production solutions that solve real business problems. You will partner directly with business and product stakeholders, translate requirements into scalable architecture, and own delivery end-to-end from design through production operations. You will move beyond traditional software engineering to design intelligent, agentic workflows that revolutionize our Go-To-Market (GTM) and customer experience processes—ensuring our platforms are the most secure and efficient in the industry, directly improving operational revenue generation.

AI Architecture & Solution Design

• Partner with business and product teams to deeply understand requirements. Translate business intent into scalable, modern technical architectures that meet non-functional requirements (performance, reliability, scalability, security, observability).
• Architect a Proxy-First AI Ecosystem—an intermediary layer that ensures vendor independence, allowing seamless switching between LLMs (e.g., GPT-4, Llama, Anthropic) without refactoring core application code.
• Build a single, unified API endpoint that abstracts the complexities of individual LLM providers, providing centralized control for access and security.
• Evaluate and choose the right architecture patterns for each use case—selecting from agentic, RAG, workflow-based, hybrid, or traditional engineering approaches based on problem characteristics.
• Make principled technology choices. Design for maintainability, extensibility, and operational excellence.

GenAI & Multi-Agent Systems

• Design and build sophisticated Multi-Agent Systems and Agent-to-Agent (A2A) workflows capable of planning, multi-step execution, self-correction, and collaboration with humans or other agents.
• Apply GenAI to complex GTM business logic—automating workflows such as customer support, sales compensation, entitlement platforms, and partner channel operations.
• Own end-to-end delivery of MarTech and FinTech initiatives, from technical design to operational readiness, leveraging AI to transform quote-to-cash processes.
• Develop retrieval systems, knowledge graph integrations, and reasoning pipelines that support autonomous task execution.

AI Observability & Quality

• Implement comprehensive observability pipelines for GenAI—tracking trace-level data, prompt inputs/outputs, model latency, and cost.
• Design architecture that routes queries to the optimal model based on complexity, balancing performance and expense.
• Pioneer "LLM as a Judge" testing methodologies—using capable models to evaluate the correctness, tone, and helpfulness of system outputs. Establish golden datasets for ground truth testing.
• Integrate observability data into the development cycle to identify bottlenecks, high-latency chains, and model drift in real-time.

Security & Governance

• Build robust guardrails to filter inputs and outputs—preventing PII exposure, offensive content, and prompt injection attacks. This is a cybersecurity company; security-first thinking is non-negotiable.
• Develop detection mechanisms including keyword filtering, behavioral analysis, and adversarial training to protect model instructions from manipulation.
• Design systems that are resilient to AI component failures with proper fallbacks.

Leadership & Communication

• Drive solution development from prototype to production, ensuring scalability, reliability, and performance.
• Communicate complex technical decisions and trade-offs clearly to both engineering peers AND business stakeholders/executives.
• Mentor and guide engineers, elevating engineering practices across the organization.
• Lead high-ambiguity, cross-team technical initiatives and drive alignment.

Qualifications

Required Experience:

• Lead / Staff - 8+ years
• Principal - 12+ years (or 8+ with Master's)
• Senior Principal - 15+ years (or 12+ with Master's, 8+ with PhD)

All Levels:

• Expert-level proficiency in Python; strong proficiency in at least one additional core language (Go, Java, Node.js, or equivalent).
• Proven, hands-on experience building applications using Large Language Models (LLMs) such as GPT-4, Llama 3, and Anthropic Claude (Haiku, Sonnet, Opus, Fable).
• Deep experience designing and building Multi-Agent systems, Agent-to-Agent (A2A) communication, and orchestration frameworks (e.g., LangChain, LangGraph).
• Proven track record building multi-tiered, enterprise-grade full-stack systems.
• Strong knowledge of distributed systems, microservice architecture, and cloud platforms (AWS/Azure/GCP).
• Demonstrated ability to partner with business/product stakeholders and translate business requirements into system designs.
• Modern software engineering practices: API design, event-driven architecture, CI/CD, observability.
• Experience delivering in Agile/Scrum environments with aggressive timelines.
• Bachelor's degree in Computer Science or related field (or equivalent experience).

Preferred Qualifications

• Proficiency with AI tracing and evaluation tools (LangSmith, Arize, HoneyHive, or custom OpenTelemetry implementations).
• Knowledge of Quote-to-Cash (Q2C) transformation and revenue operations.
• Experience with GTM, Sales, Partner/Channel Sales, or services business processes.
• Experience establishing golden datasets and performing comparative analysis across models.
• Familiarity with building supervised, unsupervised, and semi-supervised models.
• Excellent communication skills with the ability to influence at all levels.

What "Good" Looks Like

• Hear a business problem and immediately start designing the solution architecture—API contracts, data flow, AI component selection, UX considerations, SLAs.
• Choose between RAG, fine-tuning, agentic approaches, or traditional engineering based on problem characteristics—not based on what they've built before.
• Design the full stack: how the user interacts with it, how AI components integrate, how the system handles failures, how it scales, how it's monitored.
• Treat LLMs, agents, and AI capabilities as components in a larger engineering solution—understanding their constraints and trade-offs (latency, cost, reliability, hallucination risk) and designing around them.
• Communicate architecture clearly to both engineering peers and business stakeholders.
• Own the solution end-to-end, not just one layer.

Pay & Schedule

The compensation offered will depend on qualifications, experience, and work location. The offered compensation may include base salary, restricted stock units, and a bonus. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be the annual range: $145,000.00 - $235,500.00/yr.

Most of our teams work from the office full time (Santa Clara, CA HQ), with flexibility when it's needed. This role is eligible for immigration sponsorship.