Machine Learning Ops, AI Engineer

Mission Underwriting Managers, LLC; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote-first

Medical insurance

Dental coverage

Vision coverage

401K

Paid holidays

Educational assistance

Employee assistance program

Parental leave

Job summary

Mission Underwriting Managers, LLC seeks an MLOps and AI Infrastructure Engineer to build and operate production-grade AI platforms across Azure and AWS. This hands-on role emphasizes deployment automation, CI/CD, security, observability, and governance.

You will design reference architectures, establish environment standards, implement monitoring, and collaborate with security, product, data, and engineering teams to ensure production readiness and compliance.

Qualifications

• Five or more years of experience in cloud infrastructure, DevOps, MLOps, or AI platform engineering across AWS and Azure.
• Hands-on experience with Azure DevOps, Bitbucket Pipelines, and CI/CD for production systems.
• Strong experience with Terraform and Bicep, including 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, drift detection, and rollback.
• Working knowledge of AI governance, responsible AI, data privacy, model risk management, and audit or regulatory compliance.
• Experience supporting business users or working in a forward-deployed, customer-facing, or similarly stakeholder-intensive technical role.

Responsibilities

• Define and implement production-readiness standards for agentic AI platforms, including 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.
• Identify operational and governance risks early in partnership with engineering and product teams and help 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 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, and code and architecture review.
• Communicate readiness, risk, dependencies, and tradeoffs with business, delivery, product, data, security, and engineering stakeholders.
• Stay current with cloud, MLOps, AI platform, and governance practices and recommend improvements that strengthen production readiness.

Skills

MLOps

CI/CD

Cloud security

Terraform

Azure

AWS

Observability

Governance

Education

Bachelor’s degree in CS/Engineering or related field

Tools

Azure DevOps

Bitbucket Pipelines

Databricks

Job description

Mission Underwriting Managers, LLC is seeking an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure for its agentic AI platforms. This hands-on role combines cloud engineering, DevOps, AI platform operations, and AI governance to support secure, observable, scalable, resilient, and auditable AI systems across Azure and AWS.

Role Focus

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

Key Responsibilities

• Define and implement production-readiness standards for agentic AI platforms, including 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.
• Identify operational and governance risks early in partnership with engineering and product teams and help 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 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, and code and architecture review.
• Communicate readiness, risk, dependencies, and tradeoffs with business, delivery, product, data, security, and engineering stakeholders.
• 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.
• Hands-on experience with Azure DevOps, Bitbucket Pipelines, and CI/CD for production systems.
• Strong experience with Terraform and Bicep, including 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, drift detection, and rollback.
• Working knowledge of AI governance, responsible AI, data privacy, model risk management, and audit or regulatory compliance.
• Experience supporting business users or working in a forward-deployed, customer-facing, or similarly stakeholder-intensive technical role.
• Ability to deploy, operate, and troubleshoot APIs across Azure and AWS.
• Experience delivering AI solutions using LLMs, RAG, machine learning, or agent frameworks.
• Ability to deploy and manage Databricks environments in Azure using infrastructure as code.
• Strong problem-solving, written and verbal communication, and stakeholder management skills.

Preferred Qualifications

• Professional-level architecture certification in both AWS and Azure.
• Cloud architect certification or equivalent architecture experience.
• Experience with containers and orchestration, including Docker and Kubernetes.
• Experience with observability platforms and practices for distributed applications and AI workloads.
• Experience with model registries, experiment tracking, evaluation frameworks, feature management, or AI platform services.
• Experience with OAuth, JWT, API management, gateways, and secure service integration.
• Experience with data pipelines and analytics solutions using Python, SQL, and Spark is a plus.
• Experience with FinOps, cloud cost optimization, and capacity planning.
• Experience in insurance, financial services, or another regulated environment.
• Experience with Agile, DevOps, automated testing, and continuous improvement practices.
• Bachelor’s degree in computer science, engineering, information systems, or a related field, or equivalent practical experience.

Technologies and Tools

• Azure, AWS
• Azure DevOps, Bitbucket Pipelines, CI/CD
• Terraform, Bicep, policy as code
• Identity and access management (IAM), secrets management, encryption
• Monitoring, logging, tracing, observability
• Databricks
• LLMs, RAG, machine learning, agent frameworks
• APIs, Docker, Kubernetes, OAuth, JWT
• API management, gateways, Spark, Python, SQL

Benefits

• Remote-first work environment (US based)
• Medical, Dental, Vision
• 401K, 401K match
• Life and Disability benefits
• Unaccrued Paid Time Off
• 11 Paid Holidays
• Employee Discounts
• Employee Assistance Program
• Educational Assistance Program
• Employee Referral Program
• Paid Parental Leave

Location and Work Authorization

This is a remote position. Planned, in-office activities may be required occasionally (typically 2-4 times per year). You must live in the United States and be authorized to work in the United States without employment sponsorship/visa.

Working Conditions

• Requires a quiet work environment with regular sitting for extended periods while working at a computer.
• Regular use of hands and fingers.
• Frequent talking or hearing.
• Vision requirements include close, distance, color, peripheral, focus, and depth perception.

Experience Level

Minimum experience: 5 years.