The Sr.
Manager/Staff Engineer, AI Infrastructure & MLOps Engineering is a senior technical leader responsible for architecting, building, and scaling Pfizer's AI infrastructure and developer platforms.
This role leverages extensive experience in cloud engineering, DevOps, and MLOps to deliver robust, high-performance solutions supporting advanced AI/ML workloads in biotechnology, healthcare, and enterprise technology.
The successful candidate will drive innovation in automation, reliability, and scalability, enabling scientists and engineers to rapidly develop, deploy, and monitor machine learning models in production environments.
Role ResponsibilitiesPlatform Architecture & Engineering
Design, implement, and own large-scale cloud-based HPC and MLOps platforms supporting AI model training, genomic sequencing, and precision medicine.
Architect multi-environment clusters (AWS, GCP, Azure), enabling GPU/FPGA workloads and advanced observability.
Lead the development of developer and cloud platforms, including internal engineering accelerators and reusable toolsets.
Platform Catalog & Developer Experience
Design, implement, and manage unified platform catalogs using Backstage, enhancing developer experience and application metadata management.
Develop custom plugins and APIs for Backstage to support internal engineering workflows and documentation.
Automation & DevOps Excellence
Build and maintain Python-based automation frameworks, CI/CD pipelines, and Infrastructure-as-Code (Terraform, Helm, Pulumi, AWS CDK).
Operationalize containerized solutions using Docker and Kubernetes, integrating MLflow, Kubeflow, and other orchestration platforms.
Implement robust automation for provisioning, configuring, and managing cloud resources across multiple environments.
MLOps & Reliability Engineering
Lead the implementation of Service Level Indicators (SLIs), Service Level Objectives (SLOs), and advanced observability (Prometheus, Grafana, PagerDuty).
Develop and maintain APIs and services for model management, feature stores, and inference pipelines.
Operationalize ML model serving at scale using frameworks such as TensorFlow Serving, TorchServe, KServe, and Seldon Core.
Ensure compliance with industry standards (e.g., HIPAA, FDA) for data protection and reliability.
Collaboration & Leadership
Mentor engineers and lead cross-functional teams to deliver integrated solutions.
Champion engineering excellence through design documentation, code reviews, and testing automation.
Present at industry summits, author technical proposals, and contribute to open-source projects (Kubernetes, Helm, Go, Envoy).
Continuous Improvement
Drive agile delivery, sprint planning, and performance optimization.
Lead incident response and disaster recovery initiatives for mission-critical platforms.
Foster a culture of shared ownership, transparency, and innovation
Basic Qualifications
8+ years of hands-on software engineering experience in cloud infrastructure, DevOps, and MLOps.
Deep expertise in Python, Kubernetes, Terraform, Helm, and CI/CD pipeline development.
Proven experience architecting and operating containerized solutions on AWS, GCP, and Azure.
Strong knowledge of Infrastructure-as-Code, distributed systems, and production system reliability.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
Preferred Qualifications
Expertise in AWS cloud services (EC2, S3, Lambda, EKS, SageMaker, API Gateway, CloudFormation, IAM, etc.).
Experience deploying and customizing Backstage as a unified catalog for teams, services, and technical documentation.
Experience building and deploying microservices and REST/gRPC APIs for AI model delivery.
Familiarity with MLflow, Kubeflow, and other MLOps orchestration platforms.
Proficiency with model serving frameworks (TensorFlow Serving, TorchServe, KServe, Seldon Core, BentoML, etc.).
Work Location Assignment:
Remote
Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
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