Staff Cloud Engineer, ML Ops
About The Position
As a Staff MLOps Engineer at Modernizing Medicine, you’ll bridge data science, machine learning engineering, and production systems. You’ll establish architecture and tooling that enable developers to build, deploy, and operate AI-enabled applications quickly, reliably, and securely at scale. You’ll drive architectural patterns for model training, deployment, and lifecycle management, and provide self-service capabilities across product and platform teams. Your work will help shape the enterprise platform for the company’s next generation of AI-driven clinical and operational products. The role includes technical leadership and mentorship across MLOps and engineering teams, with an emphasis on system architecture, developer enablement, and operational excellence. It is hybrid, and on-call participation may be required to support production systems.
Requirements
- At least 8 years of experience in DevOps, MLOps, Software Engineering, or Cloud Architecture, with proven experience delivering and operating AI/ML systems at scale.
- Deep expertise in Python and software engineering principles for production-grade microservices, SDKs, and data pipelines.
- Proven architectural experience with patterns across the full ML lifecycle, including training, batch and real-time inference, model registration, and application integration.
- Strong proficiency with Kubernetes/EKS, serverless architectures such as Lambda, and AWS cloud infrastructure.
- Hands-on experience with ML and LLM frameworks such as MLflow, SageMaker, PyTorch, or Spark.
- Mastery of Infrastructure as Code (Terraform) and CI/CD/GitOps tooling.
- Demonstrated ability to establish enterprise observability standards using DataDog or similar platforms.
- Strong leadership, communication, and cross-functional collaboration skills, with a focus on developer enablement and mentoring.
Nice To Haves
- Experience with Databricks.
Responsibilities
- Architect, build, and operate a scalable developer enablement platform for deploying AI/ML models into high-throughput production applications.
- Define reusable patterns for model training, feature store integration, batch and real-time inference, and pipeline orchestration using Databricks, Airflow, and cloud-native services.
- Ensure ML infrastructure uses standardized infrastructure as code for repeatable, multi-environment deployments across AWS accounts.
- Lead the evolution of serverless and containerized ML execution environments, including migrating high-scale AWS Lambda pipelines to Kubernetes (EKS) microservices where appropriate.
- Drive CI/CD and GitOps standards for automated model delivery, deployment strategies, and infrastructure-as-code automation.
- Design observability and monitoring capabilities using DataDog to track model drift, latency, error rates, and production system health.
- Implement LLMOps and AIOps frameworks to manage, deploy, evaluate, and monitor LLMs and generative AI solutions, leveraging MLflow for tracking, experiment management, and model registry.
- Partner with Data Science, Product Development, and Security teams to ensure models and AI applications are secure, compliant, and cost-efficient.
- Apply security best practices to protect sensitive data and ensure compliance.
- Provide technical leadership and mentorship across MLOps and engineering teams, codifying best practices in system architecture and operational excellence.
Benefits
- Comprehensive medical, dental, and vision benefits, including a company Health Savings Account contribution.
- 401(k) matching contribution: 50% of employee contributions deferred on up to 6% of compensation; matching contributions vest fully after one year of employment.
- Generous paid time off.
- Paid parental leave programs.
- Company-paid life and disability benefits.
- Flexible Spending Account.
- Employee Assistance Programs.
- Company-sponsored Business Resource and Special Interest Groups.
- Professional development opportunities, including tuition reimbursement programs and unlimited access to LinkedIn Learning.
- Global presence and in-person collaboration opportunities.
- Dog-friendly U.S. headquarters.
- Weekly catered breakfast and lunch.
- Treadmill workstations, Zen rooms, and wellness rooms at the BRIC headquarters.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
