Senior MLOps Engineer

Requirements:

• At least 5 years of experience in DevOps, platform engineering, or site reliability engineering within SaaS environments
• Strong hands-on Azure experience, including AKS, networking, Entra identity, monitoring, and production Kubernetes operations
• Infrastructure-as-code experience using Terraform, Bicep, or similar tools, along with strong scripting skills and an automation-first approach
• MLOps experience operating production LLM or machine-learning systems, such as model gateways, inference infrastructure, or AI observability tools
• Demonstrated success identifying, explaining, and reducing cloud costs
• Experience in compliance-focused environments such as FedRAMP, StateRAMP, or SOC 2 is a plus
• Prepared to hold production access and follow disciplined practices for auditability, environment integrity, and security
• Values transparency, early risk communication, thorough ownership of shipped work, and calm, rigorous incident response

Responsibilities:

• Deploy, operate, and scale our Azure platform, including AKS, networking, identity, storage, and development-to-production environments
• Own infrastructure as code, ensuring environments are reproducible, drift is detected, and platform changes remain visible
• Operate AI infrastructure, including self-hosted observability and evaluation tools such as Langfuse, product telemetry, model gateways, workload routing, and compliant GovCloud inference paths
• Manage cloud and AI costs through metering, budgets, unit economics, MACC drawdown planning, and active cost remediation
• Strengthen production access and controls through least privilege, secrets management, audit evidence, and a FedRAMP-conscious security posture
• Partner with AI Operations on deployment and release processes, including Octopus Deploy, environment promotion, progressive rollouts, and rollback
• Build platform reliability through monitoring, alerting, incident response, and capacity planning
• Provide platform foundations for our microservices decomposition, including service infrastructure, scaling patterns, and clear environment boundaries

Technologies:

• AI
• Azure
• Cloud
• DevOps
• Support
• Kubernetes
• LLM
• Langfuse
• MLOps
• Security
• Terraform
• microservices
• Snowflake

More

We are a small, mid-stage, AI-native SaaS company helping state and local governments replace paper-based processes with intelligent digital workflows. Our platform runs on Azure and is evolving to support an AI product suite approaching general availability, self-hosted observability and telemetry within a FedRAMP-conscious boundary, autonomous agents in the delivery process, and an ongoing microservices decomposition. This is a production engineering role with access to systems serving government customers. You will own the platform where delivery workflows run and changes are deployed, while our AI Operations Engineer owns how those changes move through the delivery process. We work remotely within the United States. Our AI-native development lifecycle uses autonomous agents in planning, coding, validation, and release, with people responsible for judgment, standards, and direction. We use a Plan-and-Review cadence and value clear ownership, explainable work, and surfacing uncertainty early. Compensation is $150,000–$175,000 per year, with bonus and equity options. Benefits include medical, dental, and vision coverage with significant employer contributions for employees and dependents, company-sponsored life and disability insurance, 11 paid holidays, flexible time off, a 401(k) with a 4% employer match, and monthly home-office and wellness stipends.

last updated 43 week of 2026