Staff Engineer, AI Infrastructure

Staff Engineer, AI Infrastructure & MLOps Miejsce pracy: Warszawa Technologies we use Expected Python Kubernetes Terraform Optional AWS Your responsibilities Platform 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 Our requirements 7 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. Optional 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.). The Staff Engineer, AI Infrastructure & MLOps Engineer is a senior hands-on 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.