[Hiring] Machine Learning Ops, AI Engineer @Mission US

Role Description

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
• Governance evidence

The role partners across engineering, data, product, security, and business teams to establish repeatable production-readiness standards across all three platforms.

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, 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.

Requirements

• 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 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.

Knowledge, Skills, and Abilities

• 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.

Working Conditions

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

Additional Information

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