Senior MLOps Platform Engineer {S}

Accountabilities:

• Design, implement, and operate a unified MLOps platform spanning on-premise Kubernetes clusters and AWS, enabling rapid onboarding and consistent governance for Agentic AI services.
• Develop reusable GitLab CI and CI/CD pipelines covering model packaging, containerization, automated testing, canary deployments, and rollbacks.
• Build and maintain observability, monitoring, and alerting capabilities using tools such as Prometheus, Grafana, OpenTelemetry, and CloudWatch to track latency, throughput, resource utilization, and data drift.
• Create self-service CLIs, SDKs, and dashboards that allow data science and product teams to register models, configure inference endpoints, and manage versions with minimal DevOps support.
• Architect and maintain robust data pipelines for training data, model artifacts, and inference logs across S3 and on-premise object storage.
• Partner with research and product engineering teams to transform AI prototypes into production-grade services with strong reproducibility, security, compliance, and maintainability.
• Optimize inference performance through GPU/CPU scaling, model quantization, batching strategies, and other techniques for high-throughput workloads.
• Champion security, cost efficiency, disaster recovery, and operational best practices across hybrid infrastructure, including IAM, network policies, and secret management.
• Mentor junior engineers and contribute to technical documentation, knowledge sharing, skills development, and engineering reviews.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or a related technical field.
• 5+ years of experience building and operating production-grade software infrastructure, preferably across hybrid on-premise and cloud environments.
• Deep expertise with Kubernetes, including cluster provisioning, Helm, operators, custom resources, and container runtimes such as Docker and OCI.
• Hands-on experience with AWS services including EKS, SageMaker, S3, IAM, CloudWatch, and Step Functions, with the ability to connect on-premise resources to AWS through VPN or Direct Connect.
• Strong Python software engineering skills plus proficiency in at least one compiled language such as Go, Rust, or Java for developing platform components and SDKs.
• Experience with CI/CD and GitOps tooling such as Argo CD, Flux, GitLab, or GitHub Actions.
• Strong understanding of distributed systems, including consensus, fault tolerance, load balancing, and performance tuning for high-throughput, low-latency inference pipelines.
• Experience with data engineering technologies such as Airflow, Prefect, Kafka, Spark, or Flink and the ability to build robust, versioned data pipelines.
• Familiarity with observability platforms including Prometheus, Grafana, OpenTelemetry, or ELK, along with experience defining meaningful SLIs and SLOs for AI services.
• Demonstrated ability to collaborate with research and product teams to transition experimental code into scalable, maintainable production services.
• Strong problem-solving skills, excellent written and verbal communication, and enthusiasm for building scalable AI infrastructure.
• Working knowledge of Scrum and Agile software development methodologies is preferred.
• Ability to work remotely from within the United States. No visa sponsorship is available, and access to export-controlled information may require U.S. Person status or an applicable special license.
• Successful completion of required pre-employment screening, including credit, background, and drug screening.

Benefits:

• Remote position within the United States, supporting teams in Aurora, Colorado and King of Prussia, Pennsylvania.
• Flexible scheduling options within the pay period to support work-life balance.
• Comprehensive medical, dental, and vision insurance.
• Employer contributions to eligible HSA accounts.
• 401(k) retirement plan with strong employer contributions.
• Three weeks of vacation accrual per year, plus sick leave and time off for unscheduled life events.
• 13 paid holidays.
• Upfront tuition assistance for approved degree programs.
• Annual bonus program based on company and employee performance.
• Company-paid life insurance, AD&D, short-term disability, and long-term disability coverage.
• Four weeks of paid parental leave.
• Employee Assistance Program (EAP).