Remote MLOps & AI Infra Engineer

Mission Underwriters; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

401K

Life and Disability Benefits

11 Paid Holidays

Employee Discounts

Employee Assistance Program

Educational Assistance Program

Employee Referral Program

Job summary

Mission Underwriters is seeking an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure across our AI platforms. This hands-on role sits at the intersection of cloud engineering, DevOps, AI platform operations, and governance.

The successful candidate will ensure that our AI infrastructure is secure, observable, scalable, resilient, auditable, and ready for enterprise production use.

Qualifications

• Five or more years of experience in cloud infrastructure, DevOps, MLOps, or AI platform engineering across AWS and Azure.
• Experience with AWS and Azure cloud environments.
• Hands-on with Azure DevOps, Bitbucket Pipelines, and CI/CD for production systems.
• Strong Terraform and Bicep experience; infrastructure as code with policy as code.
• Knowledge of cloud security, IAM, networking, encryption, and disaster recovery.
• Experience deploying AI workloads and governance practices.
• Excellent communication and stakeholder management.

Responsibilities

• Define and implement production-readiness standards for AI platforms, covering deployment, security, observability, reliability, scalability, resilience, cost management, rollback, and auditability.
• Design, build, and operate secure, scalable, and resilient AI infrastructure across Azure and AWS.
• Establish reference architectures, environment standards, landing-zone patterns, and governance controls for agents, models, data, APIs, and platform components.
• Partner with engineering and product teams to identify operational and governance risks early and move prototypes into production safely.
• Build and maintain CI/CD pipelines for infrastructure, applications, models, prompts, agents, configuration, and supporting services across development, test, and production environments.
• Create reusable Terraform and Bicep modules with version control, peer review, automated validation, and policy-as-code controls.
• Automate testing, security checks, approvals, artifact versioning, environment promotion, release controls, and rollback procedures using Azure DevOps and Bitbucket Pipelines.
• Implement identity and access management, secrets management, encryption, network security, vulnerability management, configuration controls, and secure-by-default platform patterns.
• Operationalize AI governance through model and agent inventories, version tracking, approvals, risk assessments, documentation, lineage, access controls, and audit evidence.
• Partner with security, legal, compliance, and risk stakeholders to translate governance requirements into technical controls and repeatable operating practices.
• Monitor availability, latency, throughput, errors, data quality, model performance, token and cloud costs, security events, and user-impacting failures.
• Establish controls for model, data, and concept drift, prompt and configuration changes, degraded output quality, and unexpected agent behavior.
• Define thresholds, escalation paths, and remediation procedures for retraining, redeployment, rollback, and incident response; lead root-cause analysis and corrective actions.
• Design and operate cloud networking, hybrid connectivity, firewalls, DNS, load balancing, private endpoints, and secure service-to-service communication.
• Implement backup, disaster recovery, business continuity, and failover strategies appropriate to AI workloads and business-critical services.
• Plan capacity and performance and implement horizontal and vertical scaling while managing service limits, reliability, and cost.
• Provide technical direction, standards, code and architecture review.
• Work effectively with business, delivery, product, data, security, and engineering stakeholders to communicate readiness, risk, dependencies, and tradeoffs.
• Stay current with cloud, MLOps, AI platform, and governance practices and recommend improvements that strengthen production readiness.

Skills

Cloud architecture

DevOps practices

MLOps

Governance & compliance

Incident response

Stakeholder management

Python

Data pipelines

Education

Bachelor’s degree in computer science, engineering, information systems, or a related field

AWS and Azure architecture certifications

Tools

Terraform

Bicep

Azure DevOps

Bitbucket Pipelines

CI/CD pipelines

Kubernetes

Docker

Job description

Mission Underwriters is seeking an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure across our AI platforms. This hands-on role sits at the intersection of cloud engineering, DevOps, AI platform operations, and governance.

The successful candidate will ensure that our AI infrastructure is secure, observable, scalable, resilient, auditable, and ready for enterprise production use.