Senior Engineering Manager (Loyalty Product)

Zeta Global; San Francisco (CA); On-site; USD 200,000 - 260,000; Full time

Job summary

Zeta Global seeks a hands-on Staff Software Engineer to provide technical leadership for Forecasting and Recommendations platforms, focusing on production-grade AI and agentic systems. You will design, build, and operate high-throughput, low-latency distributed systems powering forecasting, recommendations, and AI-driven decisioning at scale.

The role emphasizes reliability, observability, and architectural excellence across backend services, data pipelines, and model inference, with

Qualifications

• Requires a strong foundation in software engineering and production-grade distributed systems.
• Experience with cloud infrastructure, AWS, and large-scale deployments.
• Proven ability to lead, design, and ship major services with reliability and observability.

Responsibilities

• Provide technical leadership for Forecasting and Recommendations platforms.
• Design, build, and operate high-throughput, low-latency distributed systems.
• Own services end-to-end from design through on-call and incident resolution.

Skills

Python

Java

Go

Scala

AWS

Kubernetes

CI/CD

Distributed systems

LLMs / AI workflows

Observability

Performance tuning

Team leadership

Education

Bachelor’s degree in Computer Science or related field

Tools

Docker

GitHub

Terraform

LangChain

Job description

• 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
• You will work deeply in backend systems, infrastructure, and AI application architecture, while remaining accountable for reliability, observability, and operational excellence
• 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—while enforcing production discipline
• Embrace AI-assisted development workflows (e.g., Cursor, GitHub Copilot, vibe coding paradigms) to move quickly without sacrificing quality
• Champion observability and reliability: metrics, logging, tracing, alerting, and post-incident analysis
• Lead and participate in production incident response, retrospectives, and systemic fixes
• Identify architectural risks early and make design decisions that prevent outages and scalability issues
• Reduce complexity across services, infrastructure, and processes to improve stability and team velocity
• Provide technical guidance across teams and participate in architectural reviews beyond your immediate domain

We are looking for an engineer who can contribute immediately, has shouldered real production incidents, and brings strong judgment around building stable, observable, and scalable systems, including modern agentic and LLM-powered applications2+ years of experience in Python and proficiency in one or more backend languages commonly used for large-scale systems (e.g., Python, Java, Go, Scala)Expert-level experience with AWS, including designing, deploying, and operating large-scale cloud-native systemsBachelor’s or master’s degree in computer science, Mathematics, or a related field, or equivalent practical experience12+ years of professional software engineering experience building and operating production-grade distributed systemsStrong hands-on experience with Kubernetes, containerized microservices, and modern CI/CD pipelinesExperience operating software in both on-prem data center and AWS cloud environmentsFluency with modern AI-assisted development tools (e.g., Cursor, GitHub Copilot) and comfort working in “vibe coding”–style workflows that favor fast iteration, tight feedback loops, and continuous refactoringDeep expertise in systems design, including service boundaries, concurrency, data modeling, failure handling, and scalability tradeoffsA strong track record of hands-on ownership of business-critical services, including measurable improvements in latency, throughput, stability, or costProduction experience supporting machine learning–driven systems (forecasting, recommendations, or similar), with emphasis on serving, pipelines, and infrastructureOwn and deeply understand large portions of the codebase and system architectureSimplify complex systems to increase team velocity and reliabilityInfluence standards and architectural decisions across teams through credibility and collaborationBe a prolific, hands-on contributor who raises the bar through exampleShip multiple large services, shared libraries, or major infrastructure improvementsBalance technical, analytical, and product constraints to deliver pragmatic solutionsSet short- to medium-term (6–12 month) technical direction for your domain