Sr. DevOps Engineer II

About the role

Hi Marley is transforming how the P&C industry communicates, making challenging moments for customers faster, easier, and more empathetic. We build AI-powered software that keeps everyone in the claims conversation informed and connected.

Responsibilities

• 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
• 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

Requirements

• 6-+ years of DevOps/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 or ML workloads at a SaaS company
• Naturally step up to lead technical conversations, and people across teams seek you out when infrastructure decisions get complicated
• Deep experience with AWS cloud services (ECS, Lambda, SageMaker, Bedrock, S3, DynamoDB, Redshift, or equivalent)
• Strong infrastructure-as-code skills with Terraform and understand how to manage state, modules, and multi-environment configurations
• Understand data infrastructure: pipelines, warehousing, ETL/ELT, and how to support analytics at scale
• Think about observability as more than dashboards — you care about data integrity, SLOs, error budgets, and catching silent failures
• Experience with compliance-sensitive environments and understand why audit trails, access governance, and change management matter
• Comfortable operating in a fast-moving environment where AI capabilities are evolving rapidly and infrastructure decisions have regulatory implications
• Communicate well with both engineering and non-technical stakeholders
• Track record of leading cross-team technical initiatives and mentoring engineers on infrastructure and operational best practices
• Strong proficiency in at least one programming language (Python, Go, TypeScript, or similar)
• Experience with: Container orchestration (ECS, EKS) Monitoring and observability platforms (Datadog, CloudWatch) Data infrastructure (Redshift, or similar data warehousing; Airflow, dbt, Dagster or similar pipeline tools) is a strong plus
• Experience in regulated industries (insurance, financial services, healthcare) is a strong plus
• A genuine curiosity about AI and emerging technologies, paired with the judgment to apply them thoughtfully and responsibly

Qualifications

• Compensation, Benefits & Perks

The annual base salary for this role is expected to fall within the range of [$119,000–$221,000], depending on experience, skills, qualifications, and location. Offers are determined based on these factors as well as internal peer equity. It’s most common for new hires to start near the midpoint of the range, allowing room for growth as employees develop in their role.

In addition to base pay, we offer a comprehensive total rewards package that supports both your wellbeing and professional growth, including:

• Equity grants for all employees
• A 4% matching 401(k) program
• Medical, dental, vision, disability, and life insurance coverage for employees working 30+ hours per week
• Monthly wellness stipend
• Paid parental leave
• Paid vacation

Skills

• Strong proficiency in at least one programming language (Python, Go, TypeScript, or similar)
• Experience with: Container orchestration (ECS, EKS) Monitoring and observability platforms (Datadog, CloudWatch) Data infrastructure (Redshift, or similar data warehousing; Airflow, dbt, Dagster or similar pipeline tools) is a strong plus
• Experience in regulated industries (insurance, financial services, healthcare) is a strong plus
• A genuine curiosity about AI and emerging technologies, paired with the judgment to apply them thoughtfully and responsibly

Benefits

• Medical, dental, vision, disability, and life insurance coverage for employees working 30+ hours per week
• Monthly wellness stipend
• Paid parental leave
• Paid vacation

Schedule

We are a hybrid work environment that values balance and trust. Employees are expected to work 30+ hours per week and may be required to join us in the Boston office for 2-3 days each week.

Pay

The annual base salary for this role is expected to fall within the range of [$119,000–$221,000], depending on experience, skills, qualifications, and location. Offers are determined based on these factors as well as internal peer equity. It’s most common for new hires to start near the midpoint of the range, allowing room for growth as employees develop in their role.