Machine Learning Ops, AI 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

Purpose: Mission is seeking an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure across our agentic AI platforms. This hands-on role sits at the intersection of cloud engineering, DevOps, AI platform operations, and AI governance. The successful candidate will ensure that our AI infrastructure is secure, observable, scalable, resilient, auditable, and ready for enterprise production use.

This role owns the engineering practices that move AI workloads safely from development through production, including deployment automation, CI/CD, cloud security, monitoring and observability, incident response, model and agent lifecycle controls, model drift detection, and governance evidence. The role partners across engineering, data, product, security, and business teams to establish repeatable production-readiness standards across all three platforms.

Essential Duties/Functions:

AI infrastructure and production readiness

• Define and implement production-readiness standards for the agentic 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.

DevOps, deployment, and CI/CD

• 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.

Security, governance, and risk controls

• 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.

Monitoring, observability, and model operations

• 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.

Networking, resilience, and scalability

• 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.

Technical insights and business partnership

• 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.

Required 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 mon