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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Engineering Manager, Model Infrastructure - **Company:** Harvey, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $272,000.0 - $355,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Microsoft Azure, Cloud Computing, Distributed Systems, Failover, Machine Learning, Open Source Technology, Software Engineering, AI Infrastructure, Large Language Models, Apache Spark, Model Validation, AI Platforms, Kubernetes, Apache Flink, Apache Kafka, Data Management, Machine Learning Operations, Hardware Infrastructure, Data Pipelines - **Published:** July 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=bc9c356ba7efae61 ## About the Role * 8+ years of software engineering experience, including multiple years managing high-performing engineering teams. * Experience leading teams responsible for large-scale distributed systems or cloud infrastructure. * Strong technical background that enables you to guide architectural decisions and mentor senior engineers. * Experience operating highly available production services with strong reliability and operational excellence. * Experience building platforms that require scalability, observability, automation, and cost optimization. * Strong cross-functional leadership skills with the ability to partner effectively across Engineering, Research, Product, and external vendors. * Excellent communication skills and the ability to influence technical strategy across organizations. * A passion for building teams and developing engineering talent. Nice to Have * Experience with AI infrastructure, LLM serving, or machine learning platforms. * Experience working with multiple model providers such as OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, or open-source model ecosystems. * Experience building inference platforms, model gateways, traffic routing systems, or policy-based serving infrastructure. * Experience with Kubernetes, cloud infrastructure, distributed systems, and large-scale observability platforms. * Experience supporting GPU infrastructure, model training platforms, or ML infrastructure. * Familiarity with data platforms and technologies such as Spark, Kafka, Flink, Airflow, or Iceberg. * Experience leading organizations through periods of rapid growth and technical transformation. ## Description * Lead and grow a high-performing team of software engineers responsible for Harvey's Model Infrastructure platform. * Define the technical roadmap for model reliability, scalability, and operational excellence. * Build highly reliable systems for model provisioning, capacity management, failover, and incident response across multiple AI providers. * Own Harvey's multi-provider model platform, including provider integrations, SDK upgrades, API migrations, and onboarding new model providers. * Drive the evolution of our Unified Model Controller (UMC) and Model Selector platform to automatically detect degraded models and intelligently route traffic based on health, latency, quality, compliance, and cost. * Improve observability through health dashboards, alerting, token usage analytics, cost reporting, and end-to-end model telemetry. * Partner with Product Engineering to support new model launches, capacity planning, experimentation, and proactive production monitoring. * Lead initiatives to improve inference efficiency, reduce infrastructure costs, and increase model utilization across providers. * Build the infrastructure foundation for Harvey's future model training efforts, including data pipelines, model operations, training environments, and AI platform capabilities. * Partner with executive leadership on long-term AI infrastructure strategy and vendor relationships. * Recruit, mentor, and develop exceptional engineering talent while fostering a culture of technical excellence and operational ownership. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [System Resilience: Surviving the Software Storm](https://www.wearedevelopers.com/videos/874-system-resilience-surviving-the-software-storm) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai)