Why This Job is Featured on The SaaS Jobs
This Senior AI Engineer role sits at the intersection of SaaS product delivery and the emerging shift from LLM features to agentic systems that can execute workflows. The remit spans orchestration, memory and retrieval, and real-time conversational reasoning, which are increasingly central capabilities for SaaS platforms that embed AI directly into everyday business processes. The emphasis on production-grade execution and safety guardrails reflects a maturing phase of AI adoption where reliability matters as much as novelty.
From a SaaS career perspective, the work builds durable experience in platform thinking: designing shared infrastructure that multiple product surfaces can depend on, instrumenting evaluation and observability, and integrating tool use across complex enterprise datasets. These are transferable patterns across AI-enabled SaaS, particularly for teams moving from prototypes to governed, measurable systems. The cross-functional operating model with Product, Applied Research, and Design mirrors how AI work typically ships in SaaS environments.
This role is best suited to an engineer who prefers owning architectural direction and translating ambiguous product goals into scalable systems. It will fit someone comfortable being a technical reference point, influencing standards, and mentoring peers while still building. Interest in applied AI that must perform under real-world constraints, including latency, security, and policy, is a strong signal of alignment.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your Role
As a core technical leader within our Agentic AI initiatives, you will shape the vision, architecture, and execution of Dialpad’s next-generation AI platform, moving the industry beyond passive copilots into autonomous workflow execution. Working closely with Product, Applied Research, Design, and executive leadership, you will build production-grade systems where AI agents reason, act, coordinate, and safely execute workflows.
You will help pioneer an advanced multi-agent orchestration framework capable of real-time conversational reasoning and tool execution over massive enterprise datasets, while fostering an AI-native engineering culture.
What You’ll Do
- Drive Technical Strategy: Own the architectural roadmap and delivery of Dialpad’s Agentic infrastructure, core orchestration layers, memory architectures, and evaluation/observability systems.
- Build & Scale: Design and deploy scalable, multi-modal AI agents capable of autonomous support, real-time voice reasoning, and secure API tool execution across complex enterprise workflows.
- Mentor & Influence: Act as a technical anchor for the organization, raising the engineering bar, mentoring senior peers, and defining technical standards for an AI-native SDLC.
- Partner Cross-Functionally: Collaborate with leadership across Product, Engineering, and Applied Research to align technical execution with Dialpad’s long-term business strategy.
- Push the Frontier: Research and implement emerging agent frameworks, LLM inference optimization, advanced retrieval systems, and cutting-edge safety/policy guardrails to keep Dialpad at the absolute forefront of the "era of the agent."
Skills You’ll Bring
- Experience: 8+ years of relevant software engineering experience, with a proven track record of technical leadership (as a Senior, Staff, or Principal Engineer) shipping complex, large-scale systems.
- Systems Background: Strong foundations in scaling distributed systems and production-grade infrastructure before evolving into applied AI, LLM platforms, and agentic architectures.
Core Technical Expertise:
You have shipped production systems where AI agents reason, act, coordinate, and safely execute workflows. You bring deep expertise in:
- LLM Platforms: Inference optimization and fine-tuning strategies.
- Data & Retrieval: Advanced retrieval systems and memory architectures.
- Agent Frameworks: Hands-on experience with frameworks like LangChain/LangGraph, CrewAI, or AWS/Google Agent ecosystems.
- AI Ops: Evaluation, observability, and safety frameworks for production AI systems.
- Real-Time Infrastructure: Streaming infrastructure and voice/conversational AI.
- Tool Integration: Tool use, API execution frameworks, and human-in-the-loop validation systems.
Leadership & Mindset:
- Operational Excellence: Experience setting clear technical goals, identifying architectural risks, and systematically clearing tech-debt gaps.
- The 0→1 Archetype: Ability to thrive in ambiguity, build cutting-edge AI products from the ground up, and scale them into robust, self-sustaining enterprise systems.