MLOps Engineer, Sr. — Sterling, VA

About the Company

Our client is a mid-stage, AI-native SaaS startup helping state and local governments modernize paper-based processes through intelligent, AI-driven digital workflows. As the company evolves into an AI-first platform, it is expanding its development capabilities, model iteration, and cross-team operations. A reliable, cost-disciplined platform is essential to supporting this growth while maintaining the security and reliability expected by government customers.

About the Role

We are seeking a DevOps / Platform Engineer to own the infrastructure and platform that support the company's development, validation, and release processes. This role is responsible for deploying, operating, and scaling the Azure platform, including infrastructure, environments, Kubernetes, networking, observability, and the AI serving and routing layer.

You will work closely with the AI Operations Engineer, who owns the agentic delivery system and the processes that build, validate, and release code. Your focus will be the platform those processes run on and deploy to, including its reliability, security, environment integrity, and operating costs. This is a hands-on production engineering role with production access and responsibility for maintaining least-privilege controls, audit trails, and secure environments.

Key Responsibilities

• Deploy and operate the Azure platform, including AKS, networking, identity, storage, and environments from development through production.

• Own infrastructure as code across environments, ensuring reproducibility, drift detection, and platform visibility into all environment changes.

• Operate the AI infrastructure layer, including self-hosted observability and evaluation tooling such as Langfuse, product telemetry, model gateways, per-workload routing, and compliant GovCloud inference paths.

• Manage cloud and AI costs through metering, budgets, unit economics, MACC drawdown strategy, and active cost remediation.

• Strengthen production access and security controls through least-privilege access, 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 and maintain platform reliability through monitoring, alerting, incident response, and capacity planning.

• Provide the platform infrastructure, scaling patterns, and environment boundaries needed to support the ongoing microservices decomposition.

Required Qualifications

• 5+ years of experience in DevOps, platform engineering, or site reliability engineering within SaaS environments.

• Strong Azure experience, including AKS, networking, Entra identity, and monitoring.

• Hands-on experience operating production Kubernetes environments.

• Infrastructure as code experience using Terraform, Bicep, or similar tools.

• Strong scripting skills and an automation-first approach to platform engineering.

• MLOps experience deploying and operating LLM or machine learning systems in production, including model gateways, inference infrastructure, or AI observability stacks.

• Demonstrated experience identifying, analyzing, and reducing cloud spending.

• Comfort working with production access and maintaining the security and operational discipline it requires.

Preferred Experience

Experience in compliance-heavy environments involving FedRAMP, StateRAMP, SOC 2, or similar standards.

Key Competencies

• Strong focus on environment integrity, auditability, and preventing undocumented infrastructure changes.

• Ability to understand and manage cloud costs as an engineering responsibility.

• An automation-first mindset, with an emphasis on pipelines and policies rather than manual runbook steps.

• Clear communication, early risk identification, and documentation of platform decisions and implementations.

• Ability to remain calm during production incidents and conduct thorough post-incident reviews.

• Ownership of delivered work, including the ability to explain and account for changes regardless of how they were produced.

How the Team Works

The company follows an AI-native product development lifecycle in which autonomous agents participate in planning, coding, validation, and release, while people retain responsibility for judgment, standards, and direction. The team follows a Plan-and-Review cadence rather than a ceremony-heavy Agile process.

Two expectations are central to the way the team works: engineers must understand and take ownership of everything they ship, and they must communicate openly by raising uncertainty and risks early.

What This Role Is Not

This is not a ticket-queue operations or NOC position. The role requires ownership of platform design and engineering rather than simply executing change requests designed by others. It is also not a research-focused MLOps position. The AI infrastructure supports a production product for government customers and must meet the associated reliability and compliance expectations.

Compensation and Benefits

• Annual Salary: $150,000–$175,000

• 100% remote work within the United States

• Bonus and equity options

• Medical, dental, and vision insurance, with significant employer contributions for employees and dependents based on the base-level plan; buy-up plans are available at additional cost.

• Company-sponsored life, short-term disability, and long-term disability insurance.

• 11 paid holidays

• Flexible time off

• 401(k) plan with a 4% employer match

• Monthly home office expense stipend

• Monthly wellness stipend

Equal Employment Opportunity

Jobot is an Equal Opportunity Employer. All qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information, or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment based on these protected categories.