Staff Engineer, AI Infrastructure & MLOps

Experteer Overview

This role combines hands-on engineering with cross-functional collaboration to advance Pfizer’s digital transformation and AI initiatives.

• Design, implement, and own large-scale cloud-based HPC and MLOps platforms for AI model training and precision medicine.
• Architect multi-environment clusters (AWS, GCP, Azure) for GPU/FPGA workloads and observability.
• Lead development of developer and cloud platforms, accelerators, and reusable toolsets.
• Design and manage unified platform catalogs using Backstage; develop custom plugins/APIs.
• Build and maintain Python automation frameworks, CI/CD pipelines, and Infrastructure-as-Code (Terraform, Helm, Pulumi, AWS CDK).
• Operationalize containerized solutions with Docker and Kubernetes; integrate MLflow, Kubeflow, and other orchestration tools.
• Implement provisioning and management automation across environments; support ML model serving at scale.
• Lead SLIs/SLOs and advanced observability (Prometheus, Grafana, PagerDuty) and ensure data protection and reliability (HIPAA, FDA).
• Mentor engineers, drive design reviews and testing automation; contribute to open-source projects (Kubernetes, Helm, Go, Envoy).
• Drive agile delivery, incident response, and disaster recovery for mission-critical platforms.

• 7+ years of hands-on software engineering in cloud infrastructure, DevOps, and MLOps.
• Deep expertise in Python, Kubernetes, Terraform, Helm, and CI/CD pipelines.
• Experience architecting and operating containerized solutions on AWS, GCP, and Azure.
• Strong knowledge of Infrastructure-as-Code, distributed systems, and production reliability.
• Bachelor's or Master's degree in Computer Science, Engineering, or related field.

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