Zeta global

Overview

We are hiring a hands-on Staff Software Engineer to provide technical leadership for our Forecasting and Recommendations platforms, with a strong focus on production-grade AI and agentic systems. This role centers on designing, building, and operating high-throughput, low-latency distributed systems that power forecasting, recommendations, and AI-driven decisioning at scale.

About Zeta global

Zeta Global is a technology company that provides a cloud-based marketing platform, leveraging AI and data to help businesses drive customer acquisition and retention.

Requirements & Eligibility

• 12+ years of professional software engineering experience building and operating production-grade distributed systems.
• A strong track record of hands-on ownership of business-critical services, including measurable improvements in latency, throughput, stability, or cost.
• Deep expertise in systems design, including service boundaries, concurrency, data modeling, failure handling, and scalability tradeoffs.
• Production experience supporting machine learning–driven systems (forecasting, recommendations, or similar), with emphasis on serving, pipelines, and infrastructure.
• Expert-level experience with AWS, including designing, deploying, and operating large-scale cloud-native systems.
• Strong hands-on experience with Kubernetes, containerized microservices, and modern CI/CD pipelines.
• Experience operating software in both on-prem data center and AWS cloud environments.
• Fluency with modern AI-assisted development tools (e.g., Cursor, GitHub Copilot) and comfort working in “vibe coding”–style workflows.

Key Responsibilities

• Design, build, and operate systems supporting forecasting, recommendations, and agentic AI workflows in production.
• Write production-quality code daily; own services end-to-end from design through on-call and incident resolution.
• Architect low-latency, high-throughput SaaS services, including APIs, data pipelines, model inference, and agent orchestration.
• Build and maintain production-grade agentic applications, including tool-using agents, workflow orchestration, and guardrails.
• Work fluently with foundational LLMs (e.g., GPT, Claude, Gemini Pro), selecting appropriate models and deployment patterns based on latency, cost, and reliability tradeoffs.
• Use frameworks and tooling such as LangChain, voice agents, and related ecosystems to accelerate development.
• Embrace AI-assisted development workflows (e.g., Cursor, GitHub Copilot, vibe coding paradigms).
• Champion observability and reliability: metrics, logging, tracing, alerting, and post-incident analysis.