Sr. Manager, DevOps and AI Enablement

Location

This is a hybrid role based in Palo Alto, with team members expected to work onsite one day per week for in-person collaboration.

About the role

Point is standing up a new AI Enablement function, and this dual-mandate leader will own it alongside our DevOps and platform practice. You will run the reliability, delivery, and cloud foundations a lending business depends on, while building the central hub that partners with our corporate functions (HR, Legal, Compliance, Accounting, Finance) and our Operations team (underwriters, processors, closers) to automate and augment their work with AI. Working in close partnership with IT and Security, you will set the technical direction, drive adoption, and define what success looks like across both mandates. It is a rare opportunity to shape how an entire company applies AI to its everyday work, from the platform up.

Check out our latest Engineering Blog to learn more about why it's a great time to join Point's Engineering team!

Your responsibilities

• Lead the infrastructure platform and shared-services organization, and drive key initiatives across the SRE and infrastructure functions.
• Own reliability, CI/CD, infrastructure-as-code (Terraform), cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), release tooling, and observability, including incident management and security and compliance (SOC 2).
• Stand up and run AI Enablement as the central hub: the sanctioned platform, guardrails, reusable components, and the harder builds.
• Partner with corporate functions and Operations to identify high-value workflows and design agentic AI solutions (LLM-based agents, tool calling, RAG, orchestration, and n8n / Zapier / custom integrations).
• Partner closely with IT and Security to keep enablement safe and compliant, including sanctioned tooling, access, and data-handling boundaries for sensitive and regulated functions.
• Drive adoption of what the team ships: define success metrics, baseline before building, and measure impact such as capacity reclaimed, cycle time, cost per loan, and stickiness.
• Define multi-quarter roadmaps tied to measurable business outcomes.
• Stay current on AI and automation advances, and pivot the roadmap based on user feedback and new technical capabilities.
• Set technical and quality standards through architecture guidance and review.

About you

• 10+ years in DevOps, platform, SRE, or infrastructure engineering, with strong technical depth.
• 3+ years leading and growing engineering teams, including hiring, mentoring, and performance management.
• Demonstrated experience shipping AI automations to production, including LLM agents, tool calling, RAG, and workflow orchestration.
• A track record of tying automation or platform work to quantifiable business outcomes.
• Production proficiency with infrastructure-as-code (Terraform), CI/CD, containerization (Docker, Kubernetes), and at least one major cloud (AWS, GCP, or Azure).
• Strong observability and incident-management practice (Grafana, Datadog/NewRelic, or PagerDuty).
• Technical fluency to guide and review engineering and automation work (for example Python or equivalent).
• Excellent cross-functional communication, and the ability to earn trust with non-engineering stakeholders such as Legal, Compliance, Finance, and Operations.

Nice to have

• Built a new function or team from zero in a high-growth, high-ambiguity environment.
• Fintech or other regulated-industry experience, and familiarity with PII handling and SOC 2.
• Process-improvement or RPA partnership experience.
• Lending or mortgage operations domain knowledge.

Compensation at Point will be determined by skills, experience, and geographic location. Point has identified the expected annual base salary for this role in the San Francisco Bay Area | $269,800 - $298,200

This does not include any other potential components of the compensation package, including equity, benefits, and perks outlined above. At the launch of each position, we benchmark compensation to the appropriate role and level utilizing competitive compensation data from various data sources as references. At the offer stage, we use the signal we received from our interviews, coupled with your experience, location, and other job-related factors, to determine final compensation.