Production-Ready MLOps AI Infra Engineer

Mission Underwriting Services; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Mission Underwriting Services seeks an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure across Azure and AWS. This hands-on role sits at the intersection of cloud engineering, DevOps, AI platform operations, and AI governance for enterprise AI workloads.

This role owns engineering practices to move AI workloads safely from development to production, including deployment automation, CI/CD, cloud security, monitoring, incident response, model and agent

Qualifications

• Five or more years of experience in cloud infrastructure, DevOps, MLOps, or AI platform engineering across AWS and Azure.
• Professional-level architecture certification in both AWS and Azure is preferred.
• Hands-on experience with Azure DevOps, Bitbucket Pipelines, and CI/CD for production systems.
• Strong experience with Terraform, Bicep, infrastructure as code, version control, peer review, and policy as code.
• Experience with cloud security, IAM, networking, secrets management, encryption, vulnerability management, disaster recovery, and business continuity.
• Experience implementing monitoring, logging, alerting, tracing, observability, and auditability for distributed systems.
• Practical MLOps or AI platform operations experience, including deployment, versioning, performance monitoring.

Responsibilities

• Define and implement production-readiness standards for AI platforms across 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.
• Implement end-to-end monitoring, logging, tracing, alerting, dashboards, and audit trails for AI platforms and their dependent services.
• 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

MLOps

Cloud infrastructure

DevOps

Terraform

Azure DevOps

Bitbucket Pipelines

CI/CD

Security

Observability

AI governance

Education

AWS and Azure architecture certifications

Tools

Terraform

Bicep

Azure DevOps

Bitbucket Pipelines

Job description

Mission Underwriting Services seeks an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure across Azure and AWS. This hands-on role sits at the intersection of cloud engineering, DevOps, AI platform operations, and AI governance for enterprise AI workloads.

This role owns engineering practices to move AI workloads safely from development to production, including deployment automation, CI/CD, cloud security, monitoring, incident response, model and agent