Sr. Software Engineer II; DevOps

Overview

Insurance touches people during some of the most challenging moments in their lives. Hi Marley is on a mission to transform how the P&C industry communicates, making those moments faster, easier, and more empathetic for carriers and the customers they serve. We build AI-powered software that keeps everyone in the claims conversation informed and connected. If you believe insurance can combine operational excellence and automation with a human touch, we’d love to meet you.

We are looking for a Sr. Software Engineer II (Dev Ops) to help us build and scale the infrastructure that powers both our core platform and our rapidly growing agentic AI services. You will be at the intersection of cloud infrastructure, AI operations, and platform engineering—building the foundation that enables Hi Marley to operate reliably at enterprise scale while deploying autonomous AI agents in regulated insurance workflows.

You’ll also be expected to raise the bar for the teams around you—setting infrastructure standards, driving technical decisions in ambiguous situations, and helping less experienced engineers grow their operational instincts. Teamwork and shared enthusiasm are a core part of our culture, which is why this role involves joining us in the Boston office for 2–3 days each week.

• Design and operate cloud infrastructure on AWS that supports both our core SaaS platform and our agentic AI services, ensuring reliability, scalability, and cost efficiency.
• Build and maintain AI/ML infrastructure and monitoring for LLM-powered agentic services.
• Establish and enforce infrastructure-as-code standards using Terraform, defining the patterns other engineers follow for environment parity, drift detection, and automated compliance validation.
• Implement observability beyond availability—data integrity monitoring, SLO frameworks with error budgets, and automated regression detection for both platform and AI services.
• Build deployment automation including pre-deployment verification, migration script validation, and codified rollback procedures to eliminate human-memory dependencies.
• Support big data infrastructure: data pipelines, warehousing (Redshift), and analytics tooling that enables reporting, BI, and AI training workflows.
• Implement security and compliance controls for AI workloads operating in regulated carrier environments— including audit logging, access governance, and configuration management.
• Drive environment parity across all infrastructure with automated drift detection and remediation.
• Improve disaster recovery capabilities: documented and rehearsed DR procedures, defined RTO/RPO by service tier, and tested recovery runbooks.
• Lead architecture reviews for new services, integrations, and AI agent deployments—partnering with engineering, product, and security to ensure infrastructure decisions are sound before they ship.
• Innovate on developer experience: reduce friction in testing environments, CI/CD pipelines, and local development workflows.
• Act as a technical anchor for infrastructure decisions across teams—providing clarity when requirements are ambiguous and helping the organization converge on consistent, scalable approaches.

• 6+ years of Dev Ops/SRE/Platform Engineering experience.
• 2+ years of experience building or operating AI/ML infrastructure (model serving, inference, LLM orchestration, or agentic systems).
• Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
• Built and operated infrastructure for traditional and AI/ML workloads at a SaaS company.
• Natural ability to lead technical conversations and be sought out by cross-team members when infrastructure decisions get complicated.
• Deep experience with AWS cloud services (ECS, Lambda, Sage Maker, Bedrock, S3, DynamoDB, Redshift, or equivalent).
• Strong infrastructure-as-code skills with Terraform and understanding of state, modules, and multi-environment configurations.
• Experience with data infrastructure: pipelines, warehousing, ETL/ELT, and enabling analytics at scale.
• Thoughtful approach to observability beyond dashboards—focus on data integrity, SLOs, error budgets, and catching…