Machine Learning Ops, AI Engineer

Mission US

12h•Remote

About The Position

Mission is seeking an MLOps and AI Infrastructure Engineer to build and operate production-grade infrastructure across its agentic AI platforms. This hands-on role spans cloud engineering, DevOps, AI platform operations, and AI governance. The engineer will ensure AI infrastructure is secure, observable, scalable, resilient, auditable, and ready for enterprise production use. The role owns engineering practices for moving 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 engineer partners with engineering, data, product, security, and business teams to establish repeatable production-readiness standards across the platforms. The position is remote. Planned in-office activities may be required occasionally, typically two to four times per year. Candidates must live in the United States and be authorized to work in the United States without employment sponsorship or a visa.

Requirements

At least five 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, 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, 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.
Strong knowledge of cloud architecture, MLOps, infrastructure as code, DevOps, and production operations.
Ability to design production-ready AI infrastructure and translate AI governance requirements into measurable technical controls.
Ability to use monitoring and observability signals to identify model drift, AI quality issues, reliability risks, and operational bottlenecks.
Strong understanding of security, privacy, risk management, auditability, and responsible AI practices.
Strong incident management, troubleshooting, root-cause analysis, and continuous improvement skills.
Clear communication with technical and non-technical stakeholders, including the ability to explain risk and tradeoffs.
Ability to lead through influence, mentor teammates, and establish practical engineering standards.
Strong documentation, metrics, dashboarding, and operational reporting skills.
Proficiency with Azure DevOps, Bitbucket, Terraform, Bicep, and common monitoring and observability tools.
Ability to work independently in a remote environment, manage competing priorities, and handle confidential information appropriately.
Must live in the United States and be authorized to work in the United States without employment sponsorship or a visa.

Nice To Haves

Professional-level architecture certification in both AWS and Azure is preferred.
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 building data pipelines and analytics solutions using Python, SQL, and Spark.
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.

Responsibilities

Define and implement production-readiness standards for 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 safely into production.
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 and conduct standards, code, and architecture reviews.
Work with business, delivery, product, data, security, and engineering stakeholders to communicate readiness, risks, dependencies, and tradeoffs.
Stay current with cloud, MLOps, AI platform, and governance practices and recommend improvements to production readiness.
Deploy, operate, and troubleshoot APIs across Azure and AWS.
Deploy and manage Azure Databricks environments using infrastructure as code.